WO2025153195A1 - Avatar media representation for transmission - Google Patents

Abstract

Some embodiments of a method may include: obtaining an input file, wherein the input file comprises avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information; and encoding the formatted avatar data.

Description

AVATAR MEDIA REPRESENTATION FOR TRANSMISSION

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] The present application claims benefit of European Patent Application No. EP24305093, entitled “AVATAR MEDIA REPRESENTATION FOR TRANSMISSION” and filed January 15, 2024, which is hereby incorporated by reference in its entirety.

INCORPORATION BY REFERENCE

[0002] The present application incorporates by reference in their entirety the following applications: European Patent Application Serial No. EP23306745 entitled “AVATAR MEDIA REPRESENTATION FOR TRANSMISSION” and filed October 10, 2023 (“745 application”),- European Patent Application Serial No. EP23306756 entitled “GEOMETRY AVATAR MEDIA CODEC FOR TRANSMISSION” and filed October 10, 2023 (“756 application”),- European Patent Application Serial No. EP24305092, entitled “GEOMETRY AVATAR MEDIA CODEC FOR TRANSMISSION” and filed January 15, 2024; and European Patent Application Serial No. EP24305094, entitled “AVATAR JSON INTERCHANGE FILE FORMAT” and filed January 15, 2024.

BACKGROUND

[0003] Extended reality (XR) is a technology enabling interactive experiences where the real-world environment and/or a video content is enhanced by virtual content, which may be defined across multiple sensory modalities, including visual, auditory, and haptic modalities. During runtime of the application, the virtual content (3D content or audio/video file for example) is rendered in real-time in a way which is consistent with the user context (such as environment, point of view, and device, among other things).

[0004] A media may include a coding (codec) of n-dimensional data whether computer-generated or captured from the physical world, and these can include 3D graphics objects and environments. In MPEG and other consortiums, this concept of media representation is well distinguished, and separated from delivery or presentation mechanisms, such as systems or internet protocols. As a consequence, several groups exist to delimit and progress with work on different subject problems. SUMMARY

[0005] A first example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a computer generated (CG) profile type; and encoding the formatted avatar data.

[0006] For some embodiments of the first example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0007] For some embodiments of the first example method, the codec type is one of a codec set including: VPCC, GPCC, V-DMC, gITF, and USD.

[0008] For some embodiments of the first example method, the formatted avatar data includes avatar animation data.

[0009] For some embodiments of the first example method, the formatted avatar data includes avatar geometry data.

[0010] For some embodiments of the first example method, the formatted avatar data includes avatar appearance data.

[0011] A first example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0012] A second example method in accordance with some embodiments may include: obtaining encoded avatar data, wherein the encoded avatar data is associated with a computer generated (CG) profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

[0013] For some embodiments of the second example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0014] For some embodiments of the second example method, the codec type is one of a codec set including: VPCC, GPCC, V-DMC, gITF, and USD. [0015] For some embodiments of the second example method, the decoded avatar data includes avatar animation data.

[0016] For some embodiments of the second example method, the decoded avatar data includes avatar geometry data.

[0017] For some embodiments of the second example method, the decoded avatar data includes avatar appearance data.

[0018] A second example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0019] A third example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a parametric profile type; and encoding the formatted avatar data.

[0020] For some embodiments of the third example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0021] For some embodiments of the third example method, the formatted avatar data includes weights for a parameter.

[0022] For some embodiments of the third example method, the parameter is one of a property set including: animation, shape, texture, and pose properties.

[0023] For some embodiments of the third example method, the formatted avatar data includes a parameter-based model of an avatar.

[0024] A third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0025] A fourth example method in accordance with some embodiments may include: obtaining encoded avatar data, wherein the encoded avatar data is associated with a parametric profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

[0026] For some embodiments of the fourth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0027] For some embodiments of the fourth example method, the decoded avatar data includes weights for a parameter.

[0028] For some embodiments of the fourth example method, the parameter is one of a property set including: animation, shape, texture, and pose properties.

[0029] For some embodiments of the fourth example method, the decoded avatar data includes a parameter-based model of an avatar.

[0030] A fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0031] A fifth example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is an artificial intelligence (Al) profile type; and encoding the formatted avatar data.

[0032] For some embodiments of the fifth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0033] For some embodiments of the fifth example method, the formatted avatar data includes information corresponding to a neural network.

[0034] For some embodiments of the fifth example method, encoding the formatted avatar data includes using a neural network.

[0035] A fifth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0036] A sixth example method in accordance with some embodiments may include: obtaining encoded avatar data, wherein the encoded avatar data is associated with an artificial intelligence (Al) profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

[0037] For some embodiments of the sixth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0038] For some embodiments of the sixth example method, the decoded avatar data includes information corresponding to a neural network.

[0039] For some embodiments of the sixth example method, decoding the encoded avatar data includes using a neural network.

[0040] A sixth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any one of the methods listed above.

[0041] A seventh example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the methods listed above.

[0042] An eighth example apparatus in accordance with some embodiments may include a computer- readable medium storing instructions for causing one or more processors to perform any one of the methods listed above.

[0043] A ninth example apparatus in accordance with some embodiments may include: at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the methods listed above.

[0044] An example signal in accordance with some embodiments may include encoded and formatted avatar data generated according to any one of the methods listed above.

BRIEF DESCRIPTION OF THE DRAWINGS

[0045] FIG. 1A is a system diagram illustrating an example communications system according to some embodiments.

[0046] FIG. 1 B is a system diagram illustrating an example wireless transmit/receive unit (WTRU) that may be used within the communications system illustrated in FIG. 1A according to some embodiments.

[0047] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments. [0048] FIG. 2A is a functional block diagram of block-based video encoder, such as an encoder used for Versatile Video Coding (WC), according to some embodiments.

[0049] FIG. 2B is a functional block diagram of a block-based video decoder, such as a decoder used for VVC, according to some embodiments.

[0050] FIG. 3A is a schematic side view illustrating an example waveguide display that may be used with extended reality (XR) applications according to some embodiments.

[0051] FIG. 3B is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.

[0052] FIG. 3C is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments.

[0053] FIG. 4 is a schematic illustration showing an example high-level avatar media codec according to some embodiments.

[0054] FIG. 5 is a process diagram illustrating an example top-level avatar media framework according to some embodiments.

[0055] FIG. 6 is a process diagram illustrating an example avatar encoding and decoding process according to some embodiments.

[0056] FIG. 7 is a process diagram illustrating an example avatar media cataloging process according to some embodiments.

[0057] FIG. 8 is a system diagram illustrating example top-level avatar media codec streaming component interfaces according to some embodiments.

[0058] FIG. 9 is a process diagram illustrating an example computer-generated avatar media stream process according to some embodiments.

[0059] FIG. 10 is a process diagram illustrating an example parametric avatar media stream process according to some embodiments.

[0060] FIG. 11 is a process diagram illustrating an example artificial intelligence avatar media stream process according to some embodiments.

[0061] FIG. 12 is a process diagram illustrating an example encoder architecture according to some embodiments. [0062] FIG. 13 is a process diagram illustrating an example decoder architecture according to some embodiments.

[0063] FIG. 14 is a flowchart illustrating an example process for encoding avatar data according to some embodiments.

[0064] FIG. 15 is a flowchart illustrating an example process for decoding avatar data according to some embodiments.

[0065] The entities, connections, arrangements, and the like that are depicted in— and described in connection with— the various figures are presented by way of example and not by way of limitation. As such, any and all statements or other indications as to what a particular figure “depicts,” what a particular element or entity in a particular figure “is” or “has,” and any and all similar statements— that may in isolation and out of context be read as absolute and therefore limiting— may only properly be read as being constructively preceded by a clause such as “In at least one embodiment, ....” For brevity and clarity of presentation, this implied leading clause is not repeated ad nauseum in the detailed description.

DETAILED DESCRIPTION

[0066] FIG. 1A is a diagram illustrating an example communications system 100 in which one or more disclosed embodiments may be implemented. The communications system 100 may be a multiple access system that provides content, such as voice, data, video, messaging, broadcast, etc., to multiple wireless users. The communications system 100 may enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, the communications systems 100 may employ one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single-carrier FDMA (SC-FDMA), zero-tail unique-word DFT-Spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block-filtered OFDM, filter bank multicarrier (FBMC), and the like.

[0067] As shown in FIG. 1 A, the communications system 100 may include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, 102d, a RAN 104/113, a ON 106, a public switched telephone network (PSTN) 108, the Internet 110, and other networks 112, though it will be appreciated that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and/or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and/or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d, any of which may be referred to as a “station” and/or a “STA”, may be configured to transmit and/or receive wireless signals and may include a user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular telephone, a personal digital assistant (PDA), a smartphone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (loT) device, a watch or other wearable, a head-mounted display (HMD), a vehicle, a drone, a medical device and applications (e.g., remote surgery), an industrial device and applications (e.g., a robot and/or other wireless devices operating in an industrial and/or an automated processing chain contexts), a consumer electronics device, a device operating on commercial and/or industrial wireless networks, and the like. Any of the WTRUs 102a, 102b, 102c and 102d may be interchangeably referred to as a UE.

[0068] The communications systems 100 may also include a base station 114a and/or a base station 114b. Each of the base stations 114a, 114b may be any type of device configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106, the Internet 110, and/or the other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node-B, an eNode B, a Home Node B, a Home eNode B, a gNB, a NR NodeB, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and/or network elements.

[0069] The base station 114a may be part of the RAN 104/113, which may also include other base stations and/or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), relay nodes, etc. The base station 114a and/or the base station 114b may be configured to transmit and/or receive wireless signals on one or more carrier frequencies, which may be referred to as a cell (not shown). These frequencies may be in licensed spectrum, unlicensed spectrum, or a combination of licensed and unlicensed spectrum. A cell may provide coverage for a wireless service to a specific geographical area that may be relatively fixed or that may change over time. The cell may further be divided into cell sectors. For example, the cell associated with the base station 114a may be divided into three sectors. Thus, in one embodiment, the base station 114a may include three transceivers, i.e. , one for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to transmit and/or receive signals in desired spatial directions.

[0070] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT). [0071] More specifically, as noted above, the communications system 100 may be a multiple access system and may employ one or more channel access schemes, such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, the base station 114a in the RAN 104/113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may establish the air interface 116 using wideband CDMA (WCDMA). WCDMA may include communication protocols such as High-Speed Packet Access (HSPA) and/or Evolved HSPA (HSPA+). HSPA may include High-Speed Downlink (DL) Packet Access (HSDPA) and/or High-Speed UL Packet Access (HSUPA).

[0072] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may establish the air interface 116 using Long Term Evolution (LTE) and/or LTE-Advanced (LTE-A) and/or LTE-Advanced Pro (LTE-A Pro).

[0073] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR Radio Access , which may establish the air interface 116 using New Radio (NR).

[0074] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may implement LTE radio access and NR radio access together, for instance using dual connectivity (DC) principles. Thus, the air interface utilized by WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and/or transmissions sent to/from multiple types of base stations (e.g., a eNB and a gNB).

[0075] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1 X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), and the like.

[0076] The base station 114b in FIG. 1A may be a wireless router, Home Node B, Home eNode B, or access point, for example, and may utilize any suitable RAT for facilitating wireless connectivity in a localized area, such as a place of business, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a roadway, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR etc.) to establish a picocell or femtocell. As shown in FIG. 1A, the base station 114b may have a direct connection to the Internet 110. Thus, the base station 114b may not be required to access the Internet 110 via the CN 106.

[0077] The RAN 104/113 may be in communication with the CN 106, which may be any type of network configured to provide voice, data, applications, and/or voice over internet protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have varying quality of service (QoS) requirements, such as differing throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, and the like. The CN 106 may provide call control, billing services, mobile location-based services, pre-paid calling, Internet connectivity, video distribution, etc., and/or perform high-level security functions, such as user authentication. Although not shown in FIG. 1A, it will be appreciated that the RAN 104/113 and/or the CN 106 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104/113 or a different RAT. For example, in addition to being connected to the RAN 104/113, which may be utilizing a NR radio technology, the CN 106 may also be in communication with another RAN (not shown) employing a GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0078] The CN 106 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and/or the other networks 112. The PSTN 108 may include circuit-switched telephone networks that provide plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the transmission control protocol (TCP), user datagram protocol (UDP) and/or the internet protocol (IP) in the TCP/IP internet protocol suite. The networks 112 may include wired and/or wireless communications networks owned and/or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104/113 or a different RAT.

[0079] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communications system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks over different wireless links). For example, the WTRU 102c shown in FIG. 1A may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0080] FIG. 1 B is a system diagram illustrating an example WTRU 102. As shown in FIG. 1 B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit/receive element 122, a speaker/microphone 124, a keypad 126, a display/touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and/or other peripherals 138, among others. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0081] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) circuits, any other type of integrated circuit (IC), a state machine, and the like. The processor 118 may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit/receive element 122. While FIG. 1 B depicts the processor 118 and the transceiver 120 as separate components, it will be appreciated that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0082] The transmit/receive element 122 may be configured to transmit signals to, or receive signals from, a base station (e.g., the base station 114a) over the air interface 116. For example, in one embodiment, the transmit/receive element 122 may be an antenna configured to transmit and/or receive RF signals. In an embodiment, the transmit/receive element 122 may be an emitter/detector configured to transmit and/or receive IR, UV, or visible light signals, for example. In yet another embodiment, the transmit/receive element 122 may be configured to transmit and/or receive both RF and light signals. It will be appreciated that the transmit/receive element 122 may be configured to transmit and/or receive any combination of wireless signals.

[0083] Although the transmit/receive element 122 is depicted in FIG. 1 B as a single element, the WTRU 102 may include any number of transmit/receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116. [0084] The transceiver 120 may be configured to modulate the signals that are to be transmitted by the transmit/receive element 122 and to demodulate the signals that are received by the transmit/receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11, for example.

[0085] The processor 118 of the WTRU 102 may be coupled to, and may receive user input data from, the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128 (e.g., a liquid crystal display (LCD) display unit or organic light-emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker/microphone 124, the keypad 126, and/or the display/touchpad 128. In addition, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132. The non-removable memory 130 may include random-access memory (RAM), read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from, and store data in, memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0086] The processor 118 may receive power from the power source 134, and may be configured to distribute and/or control the power to the other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel metal hydride (NiMH), lithium- ion (Li-ion), etc.), solar cells, fuel cells, and the like.

[0087] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to, or in lieu of, the information from the GPS chipset 136, the WTRU 102 may receive location information over the air interface 116 from a base station (e.g., base stations 114a, 114b) and/or determine its location based on the timing of the signals being received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by way of any suitable locationdetermination method while remaining consistent with an embodiment.

[0088] The processor 118 may further be coupled to other peripherals 138, which may include one or more software and/or hardware modules that provide additional features, functionality and/or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an e-compass, a satellite transceiver, a digital camera (for photographs and/or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands free headset, a Bluetooth® module, a frequency modulated (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a Virtual Reality and/or Augmented Reality (VR/AR) device, an activity tracker, and the like. The peripherals 138 may include one or more sensors, the sensors may be one or more of a gyroscope, an accelerometer, a hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and/or a humidity sensor.

[0089] The WTRU 102 may include a full duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for both the UL (e.g., for transmission) and downlink (e.g., for reception) may be concurrent and/or simultaneous. The full duplex radio may include an interference management unit to reduce and or substantially eliminate self-interference via either hardware (e.g., a choke) or signal processing via a processor (e.g., a separate processor (not shown) or via processor 118). In an embodiment, the WTRU 102 may include a half-duplex radio for which transmission and reception of some or all of the signals (e.g., associated with particular subframes for either the UL (e.g., for transmission) or the downlink (e.g., for reception)).

[0090] Although the WTRU is described in FIGs. 1 A-1 B as a wireless terminal, it is contemplated that in certain representative embodiments that such a terminal may use (e.g., temporarily or permanently) wired communication interfaces with the communication network.

[0091] In representative embodiments, the other network 112 may be a WLAN.

[0092] In view of FIGs. 1 A-1 B, and the corresponding description, one or more, or all, of the functions described herein may be performed by one or more emulation devices (not shown). The emulation devices may be one or more devices configured to emulate one or more, or all, of the functions described herein. For example, the emulation devices may be used to test other devices and/or to simulate network and/or WTRU functions.

[0093] The emulation devices may be designed to implement one or more tests of other devices in a lab environment and/or in an operator network environment. For example, the one or more emulation devices may perform the one or more, or all, functions while being fully or partially implemented and/or deployed as part of a wired and/or wireless communication network in order to test other devices within the communication network. The one or more emulation devices may perform the one or more, or all, functions while being temporarily implemented/deployed as part of a wired and/or wireless communication network. The emulation device may be directly coupled to another device for purposes of testing and/or may performing testing using over-the-air wireless communications. [0094] The one or more emulation devices may perform the one or more, including all, functions while not being implemented/deployed as part of a wired and/or wireless communication network. For example, the emulation devices may be utilized in a testing scenario in a testing laboratory and/or a non-deployed (e.g., testing) wired and/or wireless communication network in order to implement testing of one or more components. The one or more emulation devices may be test equipment. Direct RF coupling and/or wireless communications via RF circuitry (e.g., which may include one or more antennas) may be used by the emulation devices to transmit and/or receive data.

[0095] FIG. 1 C is a system diagram illustrating an example set of interfaces for a system according to some embodiments. An extended reality display device, together with its control electronics, may be implemented for some embodiments. System 150 can be embodied as a device including the various components described below and is configured to perform one or more of the aspects described in this document. Examples of such devices, include, but are not limited to, various electronic devices such as personal computers, laptop computers, smartphones, tablet computers, digital multimedia set top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. Elements of system 150, singly or in combination, can be embodied in a single integrated circuit (IC), multiple ICs, and/or discrete components. For example, in at least one embodiment, the processing and encoder/decoder elements of system 150 are distributed across multiple ICs and/or discrete components. In various embodiments, the system 150 is communicatively coupled to one or more other systems, or other electronic devices, via, for example, a communications bus or through dedicated input and/or output ports. In various embodiments, the system 150 is configured to implement one or more of the aspects described in this document.

[0096] The system 150 includes at least one processor 152 configured to execute instructions loaded therein for implementing, for example, the various aspects described in this document. Processor 152 may include embedded memory, input output interface, and various other circuitries as known in the art. The system 150 includes at least one memory 154 (e.g., a volatile memory device, and/or a non-volatile memory device). System 150 may include a storage device 158, which can include non-volatile memory and/or volatile memory, including, but not limited to, Electrically Erasable Programmable Read-Only Memory (EEPROM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Random Access Memory (RAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), flash, magnetic disk drive, and/or optical disk drive. The storage device 158 can include an internal storage device, an attached storage device (including detachable and non-detachable storage devices), and/or a network accessible storage device, as non-limiting examples. [0097] System 150 includes an encoder/decoder module 156 configured, for example, to process data to provide an encoded video or decoded video, and the encoder/decoder module 156 can include its own processor and memory. The encoder/decoder module 156 represents module(s) that can be included in a device to perform the encoding and/or decoding functions. As is known, a device can include one or both of the encoding and decoding modules. Additionally, encoder/decoder module 156 can be implemented as a separate element of system 150 or can be incorporated within processor 152 as a combination of hardware and software as known to those skilled in the art.

[0098] Program code to be loaded onto processor 152 or encoder/decoder 156 to perform the various aspects described in this document can be stored in storage device 158 and subsequently loaded onto memory 154 for execution by processor 152. In accordance with various embodiments, one or more of processor 152, memory 154, storage device 158, and encoder/decoder module 156 can store one or more of various items during the performance of the processes described in this document. Such stored items can include, but are not limited to, the input video, the decoded video or portions of the decoded video, the bitstream, matrices, variables, and intermediate or final results from the processing of equations, formulas, operations, and operational logic.

[0099] In some embodiments, memory inside of the processor 152 and/or the encoder/decoder module 156 is used to store instructions and to provide working memory for processing that is needed during encoding or decoding. In other embodiments, however, a memory external to the processing device (for example, the processing device can be either the processor 152 or the encoder/decoder module 152) is used for one or more of these functions. The external memory can be the memory 154 and/or the storage device 158, for example, a dynamic volatile memory and/or a non-volatile flash memory. In several embodiments, an external non-volatile flash memory is used to store the operating system of, for example, a television. In at least one embodiment, a fast external dynamic volatile memory such as a RAM is used as working memory for video coding and decoding operations, such as for MPEG-2 (MPEG refers to the Moving Picture Experts Group, MPEG-2 is also referred to as ISO/IEC 13818, and 13818-1 is also known as H.222, and 13818-2 is also known as H.262), HEVC (HEVC refers to High Efficiency Video Coding, also known as H.265 and MPEG-H Part 2), or VVC (Versatile Video Coding, a new standard being developed by JVET, the Joint Video Experts Team).

[0100] The input to the elements of system 150 can be provided through various input devices as indicated in block 172. Such input devices include, but are not limited to, (i) a radio frequency (RF) portion that receives an RF signal transmitted, for example, over the air by a broadcaster, (ii) a Component (COMP) input terminal (or a set of COMP input terminals), (iii) a Universal Serial Bus (USB) input terminal, and/or (iv) a High Definition Multimedia Interface (HDMI) input terminal. Other examples, not shown in FIG. 1 C, include composite video.

[0101] In various embodiments, the input devices of block 172 have associated respective input processing elements as known in the art. For example, the RF portion can be associated with elements suitable for (i) selecting a desired frequency (also referred to as selecting a signal, or band-limiting a signal to a band of frequencies), (ii) downconverting the selected signal, (iii) band-limiting again to a narrower band of frequencies to select (for example) a signal frequency band which can be referred to as a channel in certain embodiments, (iv) demodulating the downconverted and band-limited signal, (v) performing error correction, and (vi) demultiplexing to select the desired stream of data packets. The RF portion of various embodiments includes one or more elements to perform these functions, for example, frequency selectors, signal selectors, band-limiters, channel selectors, filters, downconverters, demodulators, error correctors, and demultiplexers. The RF portion can include a tuner that performs various of these functions, including, for example, downconverting the received signal to a lower frequency (for example, an intermediate frequency or a near-baseband frequency) or to baseband. In one set-top box embodiment, the RF portion and its associated input processing element receives an RF signal transmitted over a wired (for example, cable) medium, and performs frequency selection by filtering, downconverting, and filtering again to a desired frequency band. Various embodiments rearrange the order of the above-described (and other) elements, remove some of these elements, and/or add other elements performing similar or different functions. Adding elements can include inserting elements in between existing elements, such as, for example, inserting amplifiers and an analog-to-digital converter. In various embodiments, the RF portion includes an antenna.

[0102] Additionally, the USB and/or HDMI terminals can include respective interface processors for connecting system 150 to other electronic devices across USB and/or HDMI connections. It is to be understood that various aspects of input processing, for example, Reed-Solomon error correction, can be implemented, for example, within a separate input processing IC or within processor 152 as necessary. Similarly, aspects of USB or HDMI interface processing can be implemented within separate interface ICs or within processor 152 as necessary. The demodulated, error corrected, and demultiplexed stream is provided to various processing elements, including, for example, processor 152, and encoder/decoder 156 operating in combination with the memory and storage elements to process the datastream as necessary for presentation on an output device.

[0103] Various elements of system 150 can be provided within an integrated housing, Within the integrated housing, the various elements can be interconnected and transmit data therebetween using suitable connection arrangement 174, for example, an internal bus as known in the art, including the Inter- IC (I2C) bus, wiring, and printed circuit boards.

[0104] The system 150 includes communication interface 160 that enables communication with other devices via communication channel 162. The communication interface 160 can include, but is not limited to, a transceiver configured to transmit and to receive data over communication channel 162. The communication interface 160 can include, but is not limited to, a modem or network card and the communication channel 162 can be implemented, for example, within a wired and/or a wireless medium.

[0105] Data is streamed, or otherwise provided, to the system 150, in various embodiments, using a wireless network such as a Wi-Fi network, for example IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers). The Wi-Fi signal of these embodiments is received over the communications channel 162 and the communications interface 160 which are adapted for Wi-Fi communications. The communications channel 162 of these embodiments is typically connected to an access point or router that provides access to external networks including the Internet for allowing streaming applications and other over-the-top communications. Other embodiments provide streamed data to the system 150 using a set-top box that delivers the data over the HDMI connection of the input block 172. Still other embodiments provide streamed data to the system 150 using the RF connection of the input block 172. As indicated above, various embodiments provide data in a non-streaming manner. Additionally, various embodiments use wireless networks other than Wi-Fi, for example a cellular network or a Bluetooth network.

[0106] The system 150 can provide an output signal to various output devices, including a display 176, speakers 178, and other peripheral devices 180. The display 176 of various embodiments includes one or more of, for example, a touchscreen display, an organic light-emitting diode (OLED) display, a curved display, and/or a foldable display. The display 176 can be for a television, a tablet, a laptop, a cell phone (mobile phone), or other device. The display 176 can also be integrated with other components (for example, as in a smart phone), or separate (for example, an external monitor for a laptop). The other peripheral devices 180 include, in various examples of embodiments, one or more of a stand-alone digital video disc (or digital versatile disc) (DVR, for both terms), a disk player, a stereo system, and/or a lighting system. Various embodiments use one or more peripheral devices 180 that provide a function based on the output of the system 150. For example, a disk player performs the function of playing the output of the system 150.

[0107] In various embodiments, control signals are communicated between the system 150 and the display 176, speakers 178, or other peripheral devices 180 using signaling such as AV.Link, Consumer Electronics Control (CEC), or other communications protocols that enable device-to-device control with or without user intervention. The output devices can be communicatively coupled to system 150 via dedicated connections through respective interfaces 164, 166, and 168. Alternatively, the output devices can be connected to system 150 using the communications channel 162 via the communications interface 160. The display 176 and speakers 178 can be integrated in a single unit with the other components of system 150 in an electronic device such as, for example, a television. In various embodiments, the display interface 164 includes a display driver, such as, for example, a timing controller (T Con) chip.

[0108] The display 176 and speaker 178 can alternatively be separate from one or more of the other components, for example, if the RF portion of input 172 is part of a separate set-top box. In various embodiments in which the display 176 and speakers 178 are external components, the output signal can be provided via dedicated output connections, including, for example, HDMI ports, USB ports, or COMP outputs.

[0109] The system 150 may include one or more sensor devices 168. Examples of sensor devices that may be used include one or more GPS sensors, gyroscopic sensors, accelerometers, light sensors, cameras, depth cameras, microphones, and/or magnetometers. Such sensors may be used to determine information such as user’s position and orientation. Where the system 150 is used as the control module for an extended reality display (such as control modules 124, 132), the user’s position and orientation may be used in determining how to render image data such that the user perceives the correct portion of a virtual object or virtual scene from the correct point of view. In the case of head-mounted display devices, the position and orientation of the device itself may be used to determine the position and orientation of the user for the purpose of rendering virtual content. In the case of other display devices, such as a phone, a tablet, a computer monitor, or a television, other inputs may be used to determine the position and orientation of the user for the purpose of rendering content. For example, a user may select and/or adjust a desired viewpoint and/or viewing direction with the use of a touch screen, keypad or keyboard, trackball, joystick, or other input. Where the display device has sensors such as accelerometers and/or gyroscopes, the viewpoint and orientation used for the purpose of rendering content may be selected and/or adjusted based on motion of the display device.

[0110] The embodiments can be carried out by computer software implemented by the processor 152 or by hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The memory 154 can be of any type appropriate to the technical environment and can be implemented using any appropriate data storage technology, such as optical memory devices, magnetic memory devices, semiconductor-based memory devices, fixed memory, and removable memory, as non-limiting examples. The processor 152 can be of any type appropriate to the technical environment, and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as non-limiting examples. Block-Based Video Coding

[0111] Like HEVC, the WC is built upon the block-based hybrid video coding framework. FIG. 2A gives the block diagram of a block-based hybrid video encoding system 200. Variations of this encoder 200 are contemplated, but the encoder 200 is described below for purposes of clarity without describing all expected variations.

[0112] Before being encoded, a video sequence may go through pre-encoding processing 204, for example, applying a color transform to an input color picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of the input picture components in order to get a signal distribution more resilient to compression (for instance using a histogram equalization of one of the color components). Metadata can be associated with the pre-processing and attached to the bitstream.

[0113] The input video signal 202 including a picture to be encoded is partitioned 206 and processed block by block in units of, for example, CUs. Different CUs may have different sizes. In VTM-1.0, a CU can be up to 128x128 pixels. However, different from the HEVC which partitions blocks only based on quad-trees, in the VTM-1.0, a coding tree unit (CTU) is split into CUs to adapt to varying local characteristics based on quad/binary/ternary-tree. Additionally, the concept of multiple partition unit type in the HEVC is removed, such that the separation of CU, prediction unit (PU) and transform unit (TU) does not exist in the WC-1.0 anymore; instead, each CU is always used as the basic unit for both prediction and transform without further partitions. In the multi-type tree structure, a CTU is firstly partitioned by a quad-tree structure. Then, each quad-tree leaf node can be further partitioned by a binary and ternary tree structure. Different splitting types may be used, such as quaternary partitioning, vertical binary partitioning, horizontal binary partitioning, vertical ternary partitioning, and horizontal ternary partitioning.

[0114] In the encoder of FIG. 2A, spatial prediction 208 and/or temporal prediction 210 may be performed. Spatial prediction (or “intra prediction”) uses pixels from the samples of already coded neighboring blocks (which are called reference samples) in the same video picture/slice to predict the current video block. Spatial prediction reduces spatial redundancy inherent in the video signal. Temporal prediction (also referred to as “inter prediction” or “motion compensated prediction”) uses reconstructed pixels from the already coded video pictures to predict the current video block. Temporal prediction reduces temporal redundancy inherent in the video signal. A temporal prediction signal for a given CU may be signaled by one or more motion vectors (MVs) which indicate the amount and the direction of motion between the current CU and its temporal reference. Also, if multiple reference pictures are supported, a reference picture index may additionally be sent, which is used to identify from which reference picture in the reference picture store 212 the temporal prediction signal comes. [0115] The mode decision block 214 in the encoder chooses the best prediction mode, for example based on a rate-distortion optimization method. This selection may be made after spatial and/or temporal prediction is performed. The intra/inter decision may be indicated by, for example, a prediction mode flag. The prediction block is subtracted from the current video block 216 to generate a prediction residual. The prediction residual is de-correlated using transform 218 and quantized 220. (For some blocks, the encoder may bypass both transform and quantization, in which case the residual may be coded directly without the application of the transform or quantization processes.) The quantized residual coefficients are inverse quantized 222 and inverse transformed 224 to form the reconstructed residual, which is then added back to the prediction block 226 to form the reconstructed signal of the CU. Further in-loop filtering, such as deblocking/SAO (Sample Adaptive Offset) filtering, may be applied 228 on the reconstructed CU to reduce encoding artifacts before it is put in the reference picture store 212 and used to code future video blocks. To form the output video bitstream 230, coding mode (inter or intra), prediction mode information, motion information, and quantized residual coefficients are all sent to the entropy coding unit (108) to be further compressed and packed to form the bit-stream.

[0116] FIG. 2B gives a block diagram of a block-based video decoder 250. In the decoder 250, a bitstream is decoded by the decoder elements as described below. Video decoder 250 generally performs a decoding pass reciprocal to the encoding pass as described in FIG. 2A. The encoder 200 also generally performs video decoding as part of encoding video data.

[0117] In particular, the input of the decoder includes a video bitstream 252, which can be generated by video encoder 200. The video bit-stream 252 is first unpacked and entropy decoded at entropy decoding unit 254 to obtain transform coefficients, motion vectors, and other coded information. Picture partition information indicates how the picture is partitioned. The decoder may therefore divide 256 the picture according to the decoded picture partitioning information. The coding mode and prediction information are sent to either the spatial prediction unit 258 (if intra coded) or the temporal prediction unit 260 (if inter coded) to form the prediction block. The residual transform coefficients are sent to inverse quantization unit 262 and inverse transform unit 264 to reconstruct the residual block. The prediction block and the residual block are then added together at 266 to generate the reconstructed block. The reconstructed block may further go through in-loop filtering 268 before it is stored in reference picture store 270 for use in predicting future video blocks.

[0118] The decoded picture 272 may further go through post-decoding processing 274, for example, an inverse color transform (e.g. conversion from YCbCr 4:2:0 to RGB 4:4:4) or an inverse remapping performing the inverse of the remapping process performed in the pre-encoding processing 204. The post-decoding processing can use metadata derived in the pre-encoding processing and signaled in the bitstream. The decoded, processed video may be sent to a display device 276. The display device 276 may be a separate device from the decoder 250, or the decoder 250 and the display device 276 may be components of the same device.

[0119] Various methods and other aspects described in this disclosure can be used to modify modules of a video encoder 200 or decoder 250. Moreover, the systems and methods disclosed herein are not limited to WC or HEVC, and can be applied, for example, to other standards and recommendations, whether preexisting or future-developed, and extensions of any such standards and recommendations (including WC and HEVC). Unless indicated otherwise, or technically precluded, the aspects described in this disclosure can be used individually or in combination.

[0120] FIG. 3A is a schematic side view illustrating an example waveguide display that may be used with extended reality (XR) applications according to some embodiments. An image is projected by an image generator 302. The image generator 302 may use one or more of various techniques for projecting an image. For example, the image generator 302 may be a laser beam scanning (LBS) projector, a liquid crystal display (LCD), a light-emitting diode (LED) display (including an organic LED (OLED) or micro LED ( LED) display), a digital light processor (DLP), a liquid crystal on silicon (LCoS) display, or other type of image generator or light engine.

[0121] Light representing an image 312 generated by the image generator 302 is coupled into a waveguide 304 by a diffractive in-coupler 306. The in-coupler 306 diffracts the light representing the image 312 into one or more diffractive orders. For example, light ray 308, which is one of the light rays representing a portion of the bottom of the image, is diffracted by the in-coupler 306, and one of the diffracted orders 310 (e.g. the second order) is at an angle that is capable of being propagated through the waveguide 304 by total internal reflection. The image generator 302 displays images as directed by a control module 324, which operates to render image data, video data, point cloud data, or other displayable data.

[0122] At least a portion of the light 310 that has been coupled into the waveguide 304 by the diffractive in-coupler 306 is coupled out of the waveguide by a diffractive out-coupler 314. At least some of the light coupled out of the waveguide 304 replicates the incident angle of light coupled into the waveguide. For example, in the illustration, out-coupled light rays 316a, 316b, and 316c replicate the angle of the in-coupled light ray 308. Because light exiting the out-coupler replicates the directions of light that entered the in-coupler, the waveguide substantially replicates the original image 312. A user’s eye 318 can focus on the replicated image.

[0123] In the example of FIG. 3A, the out-coupler 314 out-couples only a portion of the light with each reflection allowing a single input beam (such as beam 308) to generate multiple parallel output beams (such as beams 316a, 316b, and 316c). In this way, at least some of the light originating from each portion of the image is likely to reach the user’s eye even if the eye is not perfectly aligned with the center of the out- coupler. For example, if the eye 318 were to move downward, beam 316c may enter the eye even if beams 316a and 316b do not, so the user can still perceive the bottom of the image 312 despite the shift in position. The out-coupler 314 thus operates in part as an exit pupil expander in the vertical direction. The waveguide may also include one or more additional exit pupil expanders (not shown in FIG. 3A) to expand the exit pupil in the horizontal direction.

[0124] In some embodiments, the waveguide 304 is at least partly transparent with respect to light originating outside the waveguide display. For example, at least some of the light 320 from real-world objects (such as object 322) traverses the waveguide 304, allowing the user to see the real-world objects while using the waveguide display. As light 320 from real-world objects also goes through the diffraction grating 314, there will be multiple diffraction orders and hence multiple images. To minimize the visibility of multiple images, it is desirable for the diffraction order zero (no deviation by 314) to have a great diffraction efficiency for light 320 and order zero, while higher diffraction orders are lower in energy. Thus, in addition to expanding and out-coupling the virtual image, the out-coupler 314 is preferably configured to let through the zero order of the real image. In such embodiments, images displayed by the waveguide display may appear to be superimposed on the real world.

[0125] FIG. 3B is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments. In an XR head-mounted display device 330, a control module 332 controls a display 334, which may be an LCD, to display an image. The headmounted display includes a partly-reflective surface 336 that reflects (and in some embodiments, both reflects and focuses) the image displayed on the LCD to make the image visible to the user. The partly-reflective surface 336 also allows the passage of at least some exterior light, permitting the user to see their surroundings.

[0126] FIG. 3C is a schematic side view illustrating an example alternative display type that may be used with extended reality applications according to some embodiments. In an XR head-mounted display device 340, a control module 342 controls a display 344, which may be an LCD, to display an image. The image is focused by one or more lenses of display optics 346 to make the image visible to the user. In the example of FIG. 3C, exterior light does not reach the user’s eyes directly. However, in some such embodiments, an exterior camera 348 may be used to capture images of the exterior environment and display such images on the display 344 together with any virtual content that may also be displayed. [0127] The embodiments described herein are not limited to any particular type or structure of XR display device.

[0128] The compression and representation of avatar/user information that is interoperable between systems and applications for transmitting and delivering digital content are described herein. A new avatar/user format to be compressed and transmitted between but not exclusive to systems, devices, and applications is described.

[0129] Digital humans may take the form of several representations (mesh, volumetric, voxel, point cloud, image, video, and sound), which is critical for the creation and formalization of digital media content. A formalized template may be created to capture different representations of a human and allow interoperable representations. This template may be, e.g., a generic human body model with a skeletal structure attached, a subject-specific model, a statistical shape model, or metadata representing the individual social properties. Either of these approaches may accurately provide a human figure as an initialization stage capable of statistically representing different human representations.

[0130] Synthetic 3D models may be used for digital human representation. A synthetic representation is usually easier to manipulate and tailor to represent a specific human anatomy, visuals, and social parameters and facilitate animation generation in immersive realities given that all parameters are known a priori. Synthetic models also facilitate appearance generalization and stylization, which professionals may use for animation and streaming.

[0131] 3D synthetic models appear to be a good representation, but they lack a standard definition and structure that may be used and matched by other digital human representations, for example, for userspecific details, such as virtual identity, social status, input device controls, the semantical structure of geometrical properties, semantical structure of shape and skeletal anatomy, but not exclusive of semantics on animation parameters.

[0132] Digital human content standardization is understood to not take into consideration human social behaviors and interaction with environmental information, and/or social and human privacy issues that are common attributes in the real world and may be adopted within social technologies. In addition, in real-time distribution or communication use cases, the encoding of digital human content and the definition of the data structure needs to be defined. In a proprietary system, these data formats are known. For open systems, the format of such data needs to be specified and standardized to enable interoperability between different systems. Described herein is a new media content format for an avatar/user representation.

Avatar Media Representation [0133] Examples of avatar representations in accordance with some embodiments are presented herein. In accordance with some embodiments, the proposed representation of an avatar is intended to be compatible with any technology such as virtual reality (VR), augmented reality (AR), extended reality (XR), streaming, gaming, interactive, collaborative, or communication systems, that considers the transmission of digital human content, e.g., 2D/3D videos, or images containing body, faces or speech, 3D technology for rendering, animating or manipulating assets of human nature, or any other system that includes social and contextual information about a real user or avatar, such as, the social and communication media industry.

[0134] The following sections detail the format of the avatar media, with the associated meaning, coding schemes, and how they may be used. In the current description, the format follows the JSON format and is compatible with the current MPEG standardization efforts to provide means for compressing and transmitting media content. However, the meaning and use are generic and may be coded with other formats (such as OpenXR, XML, and USD).

Avatar Media Ecosystem

[0135] FIG. 4 is a schematic illustration showing an example high-level avatar media codec according to some embodiments. This section in combination with FIG. 4 and Tables 1-10 provides an avatar media framework architecture/overview of top-level avatar media components.

[0136] FIG. 4 provides a high-level overview of the different components present in the avatar media codec representation. The set 400 of core components are “Metadata” 402, “Geometry” 404, “Style” 406, “Animation” 408, “Context” 410, “Physics” 412, “Speech” 414, and “Properties” 416. Each component represents a higher level of the avatar media codec. The following section will detail and introduce all the components shown in FIG. 4.

[0137] FIG. 5 is a process diagram illustrating an example top-level avatar media framework according to some embodiments. The high-level components may be part of a description of a scene or an encoding format. FIG. 5 presents mid-level components derived from the high-level components presented in FIG. 4.

[0138] FIG. 5 shows the structure of the different components and classifies them given the nature of the work. For example, a component of the example structure 500 may be classified as belonging to media 502, systems 504, or a joint collaboration 506 between the two. The following section provides detail for all the components present in FIGs. 4 and 5 and discusses the different parts of the work, which are divided into a media block, systems block, and a joint block.

Avatar Media and Systems Overview

[0139] This section introduces the concepts of media and systems for the topic of avatar media representation. It is necessary to disambiguate the concept of data stream, systems and the overlap between the two. The reasoning for this separation is so the work within avatar media representation may be clearly understood and efficiently represented.

[0140] A media may include a coding (codec) of n-dimensional data whether computer-generated or captured from the physical world, and this data may include 3D graphics objects and environments. In MPEG and other consortiums, media representation is separate from delivery or presentation mechanisms, such as systems or internet protocols. As a consequence, several groups exist to delimit and work on different subject problems.

[0141] On the other hand, systems are a coded representation of encapsulation formats, delivery protocols, multimedia presentation information, and schemes for declaring and describing multimedia content. FIG. 7 illustrates the difference between these two concepts and demonstrates the need for the two on the same topic. Although these two concepts have different mandates, there is still a frontier between the two that is sometimes not clear and may overlap, as illustrated in FIG. 5.

[0142] FIG. 6 is a process diagram illustrating an example avatar encoding and decoding process according to some embodiments. For some embodiments of an example architecture 600, raw avatar and scene description data resides on a server 602. The raw scene description data may be retrieved, formatted, and processed by a server, which may be different from the server storing the raw data. Likewise, raw avatar data may be retrieved, formatted, and processed by a server (which may be different from the server storing the raw data) to obtain avatar model data 604, avatar animation data 606, and avatar metadata 608. These three sets of avatar data may be encoded using an avatar encoder 610. Also, these three sets of avatar data may be sent to a scene description processing block 612 for further processing. The output of the avatar encoder may undergo formatting and binary coding 614 to generate an encoded avatar binary file. This encoded avatar binary file may be sent to a client device. The client device 616 may decode 618 the file to obtain the avatar data. The client device may also obtain scene description data. The client device 616 may use both of these data items to render 620 an augmented reality scene environment combined with an avatar.

[0143] FIG. 7 is a process diagram illustrating an example avatar media cataloging process according to some embodiments. The left side of the system 700 of FIG. 7 shows a codec block 702 that is agnostic to the meaning of the data. This codec block 702 is not capable of interpreting the applicability or the dissimilarities between the different data channels. The codec interprets the different input formats 704 equally and stores them in similar data structures. The structure of a channel 708 contains properties that allow a system model 706, such as the one on the right side of FIG. 7, to correctly interpret the meaning of the channel 708 or packet data. The example in FIG. 7 shows a mapping of a “geometry” (left side) packet 710 flagged with “FlagJD: 0” with a system object “Geometry” (right-side) 712. In this manner, the system object understands the streamed content as geometrical information and handles the content accordingly.

[0144] FIG. 7 shows the differences and overlap of media and systems. For media, the objective is to compress and efficiently transmit data. For systems, the objective is to process and handle the media according to the nature of its properties.

Avatar Media Description

[0145] FIG. 8 is a system diagram illustrating example top-level avatar media codec streaming component interfaces according to some embodiments. FIG. 8 illustrates what types of data may be streamed over a period of time or on a single instance. The blocks shown in FIG. 8 represent the core blocks of the avatar media codec format. FIG. 8 shows a structure 800 with a focus on encoding and transmitting static and timebased animation, geometric, appearance, and/or artificial intelligence data.

[0146] The encoding may be “time-based” or “key-frame based”. The encoding encapsulates at least one of the profiles of Computer Graphics (CG) 802, Parametric Model 804, or Artificial Intelligence (Al) Model 806.

[0147] Time-based data transmission assumes a continuous stream of timed media without the necessity of intra-frame interpolation. Each frame transmitted is assumed to be continuous in time and consequently temporally aligned.

[0148] Key-frame data transmission assumes a discontinuous stream of timed media with the possibility for intra-frame or inter-frame interpolation between, either key-frames or reference frames. A key-frame is assumed to be data received with respect to a single time instance, which may be in the future, present, or past. A reference frame is assumed to be data transmitted one or several times as a key-frame flagged as a reference frame and to be kept in memory or used for inter-frame interpolation.

[0149] The encoding is agnostic to the type of data being compressed. As a consequence, whether the data is time or key-frame based does not affect the encoded data for transmission. The encoding may follow three major profiles, but not exclusive. The major profiles are CG, Parametric Model, and Al Model.

Computer Graphics (CG) Profile

[0150] The CG profile follows a “traditional” computer graphics ideology and includes animation, geometry, and appearance information.

[0151] Animation data transmission handles dynamic information that is to be updated or continuously streamed. Animation data may include (but not exclusively): joint animation 808, blendshape weights 810, controllers weights 812, vertex displacements 820, vertices 822, landmarks 832, motion dictionary 814, texture/materials 816, UV displacements 818, shape weights, and other related information.

[0152] Geometry data transmission handles static information that only requires transmission for handshakes and model updates. Geometry data may include (but not exclusively): vertices 822, faces 830, skeleton 824, landmarks 832, UV maps 826, skinning 834, texture/materials 816, blendshapes 828, vertex displacements 820, and other related static data.

[0153] Appearance data transmission handles texture and appearance information that needs either continuous or key-frame-based streaming. Appearance data may include (but not exclusively): video, image, UV displacements 818, textures 816, and other related appearance data.

Parametric Model Profile

[0154] The parametric model profile is based on a non-AI-based mechanism, but instead is based on probabilistic models that are data-driven and parametrized with known characteristics. For example, one model may have animation weights 836 that directly modify and drive the model's animation system, such as making an avatar walk, or making the avatar model reach for objects in the scene. Shape weights 838 may make the avatar skeletal, body, or garment change into different visual forms while preserving the essence of the model (topology). Texture weights 840 may make the visual appearance change or animate without interfering with the geometry. Pose weights 842 may change the pose of the avatar through predefined rigg parameters on the shape and skeletal structure.

Artificial Intelligence (Al) Model Profile

[0155] The Al model profile follows a more recent and sophisticated approach that relies upon neural deep networks to encode, decode, and represent avatar information.

[0156] A deep network 844 may be used for encoding and making reference to its architecture 848, architecture weights after training (which may include network weights 846 for some embodiments), latent features 856, and its semantical definition. The latent features may include the following categories (which were previously stated in the CG profile), including: animation 850, geometry 852, and texture 854, or may have an independent definition defined by the model itself. Since a neural network may handle multiple categories simultaneously, a single category definition may not translate correctly to the functionality of the model. Hence, the model itself may define its definition and provide a mapping mechanism so that a system may correctly translate and apply this data.

Avatar Media Stream Architecture [0157] The architecture for the avatar media stream identifies various elements and interfaces between different blocks. The following sections depict the architecture for each of the profiles.

CG Avatar Media Stream Architecture

[0158] FIG. 9 is a process diagram illustrating an example computer-generated avatar media stream process according to some embodiments. FIG. 9 shows an architecture 900 for avatar media streaming that has a context more focused on computer-generated (CG) content. Existing standards and platforms to encode and transmit geometrical information (e.g., VPCC, GPCC, VDMC, gITF, USD) may be evaluated for usability and performance.

[0159] FIG. 9 contains the components used for encoding and transmitting avatar information, which is interoperable across systems and platforms. The input block 902 in FIG. 9 shows examples of different inputs that may be used simultaneously or as individual content inputs. The processing block 904 uses algorithms and/or methods to extract information from the input source(s) for a particular codec format (e.g., VPCC, GPCC, V-DMC, gITF, and USD). The priors block 906 includes models that are used to infer template-based information about the content of the input source. The data format block 908 defines the elements and data of the avatar media codec. The encode block 910 and decode block 912 each use compression / decompression, and compacting / de-compacting processes to allow efficient and lightweight bitstream transmission of the avatar media codec. The output block 914 uses algorithms and/or methods to transform the data into a format used by an application. The application block 916 uses the avatar data, such as by 2D/3D rendering or other means to display and/or interact with digital content.

Data Format

[0160] The data format includes all core elements shown in FIG. 4 and FIG. 5. In the context of the CG profile, the data may include (but not exclusively) the following geometry, animation, appearance, and style properties.

[0161] Vertices 918 may be expressed in the form of 2D/3D spatial descriptors that represent the 2D or 3D geometrical composition of an avatar.

[0162] Faces 920 may be expressed in the form of triangular or quadratic descriptors that represent the vertex connectivity (topology) of the avatar.

[0163] A skeleton 922 may be expressed in the form of 2D/3D spatial descriptors that represent the 2D or 3D skeletal joints composition of an avatar. Skeletal connectivity in the form of a hierarchical tuple composition composed of joint indices may be used to create a skeletal bone representation of the skeletal joints

[0164] Skinning 928 may be expressed in the form of a weighted matrix representation of the relationship between joints and vertex indices. For each vertex a joint/bone is associated with a scalar weight between 0 and 1 , being 0 with no influence on the vertex and 1 with maximum influence on the vertex. For every vertex, the sum of all the joints/bones scalar weight shall be equal to one.

[0165] Landmarks 924 may be expressed in the form of 2D/3D spatial descriptors that represent the 2D or 3D landmarks on the surface of the avatar. A landmark may include a weighted matrix representation to link a surface of the geometry (a mesh) with an animation technique.

[0166] UV maps 926 may be expressed in the form of 2D spatial descriptors that map the 3D geometric representation onto the 2D domain.

[0167] Blendshapes 930 may be expressed in the form of 2D/3D spatial descriptors that represent 2D or 3D displacement vectors of the original geometry. A collection of blendshapes may represent an animation of the avatar geometry. For example, facial expressions are built with a collection of blendshapes and may be used by an application to infer a personalized animation. Such an inference may be done by linearly or non-linearly combining several blendshapes. A blendshape may include high-level controllers, which may be predefined to act on one or several blendshapes together in a linear or non-linear manner. As a result, an application may use blendshapes for accurate and realistic animations.

[0168] Joint animation 932 may be expressed as a hierarchical matrix representation, quaternions, or Euler angles of the joints transformation matrix, including translation, rotation and scale.

[0169] Blendshape weights 936 may be expressed as a scalar value with ranges between a minimum and a maximum value linearly defined by the blendshape deformations. Such weights may represent a state transition from a “neutral” pose to a “target” pose. The “neutral” pose is a binding pose from which the blendshape was calculated, and the “target” pose is the blendshape deformation.

[0170] Controller weights 938 may be expressed as 1 D, 2D, or 3D scalar values that linearly or non- linearly combine several deformations vectors or blendshapes to animate a specific geometric location.

[0171] A motion dictionary 934 may be expressed as a semantical dictionary or tree that maps a human- readable descriptions (e.g., walk, jump or idle) to animation parameters (e.g., joint animations, blendshape weights, or controller weights). [0172] Texture and material 940 may be expressed in 2D image file formats (e.g., png, jpeg, among others) that represent the visual appearance of the avatar.

[0173] UV displacements 942 may be expressed as 2D spatial descriptors that map a 3D geometric representation to a 2D domain. Such mappings provide a mechanism to deform, animate, and/or stylize a geometric and/or appearance representations of an avatar.

[0174] Vertex displacements 944 may be expressed as 3D spatial descriptors that provide a mechanism to deform, animate, or stylize a geometric representation of an avatar.

Parametric Avatar Media Stream Architecture

[0175] FIG. 10 is a process diagram illustrating an example parametric avatar media stream process according to some embodiments. FIG. 10 shows an architecture 1000 for avatar media streaming that has a context more focused on parametric models content. Existing standards and platforms to encode and transmit geometrical information (e.g., VPCC, GPCC, VDMC, gITF, USD) may be evaluated for usability and performance.

[0176] FIG. 10 contains the components used for encoding and transmitting avatar information, which is interoperable across systems and platforms. The input block 1002 in FIG. 10 shows examples of different inputs that may be used simultaneously or as individual content inputs. The processing block 1004 uses algorithms and/or methods to extract information from the input source(s) for a particular codec format (e.g., VPCC, GPCC, V-DMC, gITF, and USD). The priors block 1006 includes models that are used to infer template-based information about the content of the input source. The data format block 1008 defines the elements and data of the avatar media codec. The encode block 1010 and decode block 1012 use compression / decompression, and compacting / de-compacting processes to allow efficient and lightweight bitstream transmission of the avatar media codec. The output block 1014 uses algorithms and/or methods to transform the data into a format used by an application. The application block 1016 uses the avatar data, such as by 2D/3D rendering or other means to display and/or interact with digital content.

Data Format

[0177] The data format includes all core elements shown in FIG. 4 and FIG. 5. In the context of the parametric profile, the data may include (but not exclusively) the following animation, shape, texture, and pose properties.

[0178] Animation weights 1018 may be expressed as 1 D, 2D, or nD (for any integer greater than 0) descriptors that represent parameters to animate the geometry and/or skeletal representation of the avatar. Such descriptors are model-based and not transferable across different models. These descriptors may have several forms, such as, a form with an associated semantical meaning or a form that is more numerical.

[0179] Shape weights 1020 may be expressed as 1 D, 2D, or nD (for any integer greater than 0) descriptors that represent parameters to deform the geometry representation of the avatar. Such descriptors are modelbased and not transferable across different models. These descriptors may have several forms, such as, a form with an associated semantical meaning or a form that is more numerical.

[0180] Texture weights 1022 may be expressed as 1 D, 2D, or nD (for any integer greater than 0) descriptors that represent parameters to deform the texture and/or appearance representation of the avatar. Such descriptors are model-based and not transferable across different models. These descriptors may have several forms, such as, a form with an associated semantical meaning or a form that is more numerical.

[0181] Pose weights 1024 may be expressed as 1 D, 2D, or nD (for any integer greater than 0) descriptors that represent parameters to deform the geometry and/or skeletal pose representation of the avatar. Such descriptors are model-based and not transferable across different models. These descriptors may have several forms, such as, a form with an associated semantical meaning or a form that is more numerical.

Artificial Intelligence (Al) Avatar Media Stream Architecture

[0182] FIG. 11 is a process diagram illustrating an example artificial intelligence avatar media stream process according to some embodiments. FIG. 11 shows an architecture 1100 for avatar media streaming that has a context more focused on Artificial Intelligence (Al) content. Existing standards and platforms to encode and transmit geometrical information (e.g., VPCC, GPCC, VDMC, gITF, USD) may be evaluated for usability and performance.

[0183] FIG. 11 contains the components used for encoding and transmitting avatar information, which is interoperable across systems and platforms. The input block 1102 in FIG. 11 shows examples of different inputs that may be used simultaneously or as individual content inputs. The processing block 1104 uses algorithms and/or methods to extract information from the input source(s) for a particular codec format (e.g., VPCC, GPCC, V-DMC, gITF, and USD). The priors block 1106 includes models that are used to infer template-based information about the content of the input source. The data format block 1108 defines the elements and data of the avatar media codec. The encode block 1110 and decode block 1112 use compression / decompression, and compacting / de-compacting processes to allow efficient and lightweight bitstream transmission of the avatar media codec. The output block 1114 uses algorithms and/or methods to transform the data into a format used by an application. The application block 1116 uses the avatar data, such as by 2D/3D rendering or other means to display and/or interact with digital content. Data Format

[0184] The data format includes all core elements shown in FIG. 4 and FIG. 5. In the context of the artificial intelligence profile, the data may include (but not exclusively) the following animation, shape, texture, and pose properties.

[0185] A deep network may be expressed as a pre-trained neural network that uses input features (video, image, or shape, among others) to represent a given task for avatar streaming, representation, and/or manipulation. A deep network may include mechanisms for encoding and decoding through a combination of linear or convolutional layers. A deep network may use a latent feature representation of encoded data on the receiver side along with a decoder. For a given latent feature, the decoder may generate an output that is given to the network while in training mode.

[0186] An architecture 1118 may be expressed in a binary or human-readable (JSON) format that includes the format and representation of all the layers in the network. An architecture may include encoders, decoders, and/or generators. The architecture includes details for all attributes related to such layers, such as, input and output parameters, linear and convolutional layers, activation mechanism types (if any), normalization mechanism types, skip connection details, arithmetics between layers (the multiplying, adding, subtracting, and/or dividing of: layers, weights, parameters, and/or outputs), and initialization parameters.

[0187] The network weights 1120 may be expressed in a binary or human-readable (JSON) format that includes all weights and bias parameters of the network architecture. These weights initialize the network architecture to a state capable of being instantiated (e.g., the state of the network when training was satisfied/completed).

[0188] The animation, geometry, and texture parameters may take the form presented above in the context of a CG profile and may be available at the receiver or application side of the framework. The transmitted information may relate to one or more properties of the data presented above in the context of a CG profile. To aid a decoder or application in reading the stream, a label may accompany a latent feature to indicate which type of data the decoder or application is expected to handle (e.g., an “Animation”, ” JointAnimation”, or other latent feature). Such a label may announce to the application or decoder that the latent feature is a joint animation. Such a joint animation may be a joint rotation, translation, and/or scale.

[0189] Latent features 1122 may be expressed as 1 D, 2D, or nD (for any integer greater than 0) descriptors that represent parameters to deform one or several components of an animation, a geometry and/or a texture/appearance representation of an avatar. Such descriptors are Al-based and not transferable across different networks without re-training. These descriptors may have several forms, such as, a form with an associated semantical meaning or a form that is more numerical.

Interchange File Format

[0190] The interchange format may be a JSON format or another text-form implementation of the data model. This format is human-readable and may be manipulated by a user. For some embodiments, the format is Avatar JSON Interchange Format (AJIF). AJIF information about digital humans (avatars) may be encoded and represented in a stream between systems. Table 1 illustrates higher level information available for streaming and representing of avatar media.

Avatar Encoder API

[0191] Shown below is an example application programming interface (API) to encode avatar media content.

Geometry

[0192] The geometry of an avatar media represents properties that define the avatar asset and semantical meaning. This may be in the form of connected or disconnected vertices, skeletal structures, 3D markers, images, or contextual information. The function “processGeometryO” handles the fetching of (relevant) information. [0193] In some embodiments, semantics may include labeling of vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatar-object interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

[0194] The objective of this signal is to inform the decoder that geometric information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric information, including MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 2: Geometry Semantics

Style

[0195] The shape of an avatar media represents the properties that define the avatar style, such as clothes, hairstyle, accessories, visual appearance, personality and behavior. The function “processstyle ()” handles the fetching of (relevant) information.

[0196] In some embodiments, style may include the style of motion an avatar may perform, such as, for example, the avatar has an "erratic" style or "dunk" style. In terms of animation styles, style may be the clothes or wearables the avatar may have available, style may be the hair style the avatar has, style may be the shape style of the avatar. Even one’s own personality may be considered a style in society.

[0197] The objective of this signal is to inform the decoder that style information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary information, including MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 3: Style Semantics

Animation

[0198] The animation of an avatar media represents the properties that define the avatar motion, such as in the form of 2D video, immersive video, multi-view video, skeletal or geometry. The function “processAnimationO” handles the fetching of (relevant) information.

[0199] In some embodiments, an animation avatar media element may be related to the animation parameters, for example, styles may be correlated with motion and have specific motion vectors available to certain styles of animation. In some embodiments, animation may be related to skeletal animation, geometry displacement either rigid or non-rigid. In some embodiments, animation may be correlated with the garments motion of the avatar if existing.

[0200] The objective of this signal is to inform the decoder that animation information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary information, such as MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 4: Animation Semantics Context

[0201] The context of an avatar media represents the properties that define the scene in which the avatar is included, such as a description of the world environment within a VR or AR system as a video stream or in a scene description format. The function “processContextO” handles the fetching of (relevant) information.

[0202] In some embodiments, scene of the avatar includes the surroundings that the avatar is currently at. The scene may be, for example, the background in the context of a 2D video stream, that is to be preserved while streaming with the avatar data, or to be removed according to the signal present.

[0203] The objective of this signal is to inform the decoder that context information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary information, such as MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 5: Context Semantics

Physics

[0204] The physics of an avatar media represents the properties that define the avatar motion and scene interaction based on physical models of the avatar and its environment. These properties may describe the gravity force, wind forces, or other natural forces, as well as lighting or material properties. The function “processPhysicsQ” handles the fetching of (relevant) information.

[0205] The objective of this signal is to inform the decoder that physics information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary information, such as MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 6: Physics Semantics

Haptics

[0206] The haptics of an avatar media represents the properties that define the haptic feedback features available for an avatar. These features may take the form of object material properties, haptic sensors on the avatar, potential feedback responses from other objects and systems in an environment, haptic signals attached to specific body parts or a sensitivity map for instance. The function “processHapticsQ” handles the fetching of (relevant) information.

[0207] In some embodiments, haptics may include the signal of tactile sensation that the avatar might have if hardware is available. Examples include wearing gloves for tactile sensation or wearing a haptics vest for impact simulation.

[0208] The objective of this signal is to inform the decoder that haptic information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary haptic information, such as MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, HMPG, H JI F and others. able 7: Haptics Semantics Audio

[0209] The audio of an avatar media represents the properties that define the avatar audio, such as speech and noise by contact or spatial audio properties. The function “processAudioQ” handles the fetching of (relevant) information.

[0210] In some embodiments, audio properties may include audio related to spatial audio or speech output available to be transmitted.

[0211] The objective of this signal is to inform the decoder that audio information is available for reading. This signal may take many forms, such as traditional encoding standards for audio, such as MPEG AVC, HEVC, WC, and others.

Metadata

[0212] The metadata of an avatar media represents the properties that allow signal and data decoding from other entities, such as other standards (MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H- anim, JVET). The function “processMetadaQ” handles the fetching of (relevant) information.

[0213]

Table 9: Metadata Semantics

Properties

[0214] The properties of an avatar media represent additional information that may further define the avatar being transmitted, for example, social or personal information, or additional information relevant to the application that receives the data content that may possibly not be included in the specifications (for example, proprietary physics properties, proprietary semantics of the avatar geometry, and others). The function “processPropertiesO” handles the fetching of (relevant) information.

[0215] In some embodiments, the properties avatar media element may include for example social and personal user information, along with for example additional information that may be needed to, e.g., encode, transmit, and/or decode any information present in the bitstream or in processing.

[0216] The objective of this signal is to inform the decoder that properties information is available for reading. This signal may take many forms, such as traditional encoding standards for geometric and complementary information, such as MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H-anim, JVET, and others.

Table 10: Properties Semantics

Avatar Decoder API

[0217] Shown below is an example application programming interface (API) to decode avatar media content and handle information coming from an encoder.

Geometry [0218] The geometry of an avatar media represents properties that define the avatar asset and semantical meaning. This may be in the form of connected or disconnected vertices, skeletal structures, 3D markers, images, or contextual information. The function “decodeGeometryO” handles the fetching of (relevant) information transmitted by the encoder.

[0219] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Style

[0220] The shape of an avatar media represents the properties that define the avatar style, such as clothes, hairstyle, accessories, visual appearance, personality and behavior. The function “decodeStyleQ” handles the fetching of (relevant) information transmitted by the encoder.

[0221] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Animation

[0222] The animation of an avatar media represents the properties that define the avatar motion, such as in the form of 2D video, immersive video, multi-view video, skeletal or geometry. The function “decodeAnimationO” handles the fetching of (relevant) information transmitted by the encoder.

[0223] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Table 13: Animation Semantics

Context

[0224] The context of an avatar media represents the properties that define the scene in which the avatar is included, such as a description of the world environment within a VR or AR system as a video stream or in a scene description format. The function “decodeContextO” handles the fetching of (relevant) information transmitted by the encoder.

[0225] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatar- object interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Table 14: Context Semantics

Physics

[0226] The physics of an avatar media represents the properties that define the avatar motion and scene interaction based on physical models of the avatar and its environment. These properties may describe the gravity force, wind forces, or other natural forces, as well as lighting or material properties. The function “decodePhysicsQ” handles the fetching of (relevant) information transmitted by the encoder.

[0227] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Table 15: Physics Semantics

Haptics [0228] The haptics of an avatar media represents the properties that define the haptic feedback features available for an avatar. These features may take the form of object material properties, haptic sensors on the avatar, potential feedback responses from other objects and systems in an environment, haptic signals attached to specific body parts or a sensitivity map for instance. The function “decodeHapticsQ” handles the fetching of (relevant) information transmitted by the encoder.

[0229] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Audio

[0230] The audio of an avatar media represents the properties that define the avatar audio, such as speech and noise by contact or spatial audio properties. The function “decodeAudioQ” handles the fetching of (relevant) information transmitted by the encoder.

[0231] In some embodiments, semantics may include decoding of audio properties that may include audio related to spatial audio or speech.

Table 17: Audio Semantics

Metadata

[0232] The Metadata of an avatar media represents the properties that allow signal and data decoding from other entities, such as other standards (MPEG-I SD, MPEG-AI MPEG V-DMC, L-PCC, MPEG 4, H- anim, JVET). The function “decodeMetadataQ” handles the fetching of (relevant) information transmitted by the encoder.

[0233] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatarobject interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Properties

[0234] The properties of an avatar media represent additional information that may further define the avatar being transmitted, for example, social or personal information, or additional information relevant to the application that receives the data content that may possibly not be included in the specifications (for example, proprietary physics properties, proprietary semantics of the avatar geometry, and others). The function “decodePropertiesQ” handles the fetching of (relevant) information transmitted by the encoder.

[0235] In some embodiments, semantics may include decoding of labels for vertices, faces, regions (e.g., geometry), landmarks and skeletal labeling, texture labeling, for applications such as classification; avatar- object interaction, avatar-avatar interaction, avatar retargeting (e.g., animation, style, geometry, and the other elements shown in FIG. 4 and Tables 2-10).

Table 19: Properties Semantics

Encoder

[0236] FIG. 12 is a process diagram illustrating an example encoder architecture according to some embodiments. FIG. 12 illustrates in more detail the encoder architecture 1200 for the avatar media codec and properties described above. The encoder is able to process multiple types of input files 1202, descriptive avatar media files (such as Graphics Library Transmission Format (.gltf) 1204, Extensible Three-Dimensional format (.x3d) 1206, JavaScript Object Notation (.json) 1208, Extensible Markup Language format (.xml) 1210, and text format (.txt) to name a few formats) which contain the information presented above. The output data from the encoder may have multiple forms. For some embodiments, as illustrated in FIG. 12, two encoder output formats are used: a human readable format (based on JSON in this example implementation) and a binary format. The JSON-based format (which may be stored in JSON-based file 1212 for some embodiments) provides metadata information for each of the elements of the avatar data structure (such as geometry, style, animation, and the other elements shown in FIG. 4 and Tables 2-10) and references the appropriate file. The binary format compresses (all) the data in a single binary file 1214. In this format, the data may be “packetized” to allow independent access to each of the elements of the avatar data structure. In accordance with some embodiments, “Packetization” 1222 in this context should be understood as grouping the elements of the avatar data structure into separate blocks that contain, e.g., metadata information such as the metadata information described in Tables 2-10, and that may encapsulate the associated data after binary compression 1220. In some embodiments, packetization 1222 may include, for example, organizing the binary compressed data into dedicated packets for each of the elements of the data structure (e.g., geometry, style, and the like). Such a feature may, for example, be used during transport to request access to part of the data only. For some embodiments, the data may be compressed into 2 or more binary files. Other encoding schemes and associated data formats to represent avatar data not shown in FIG. 12 also may be used for some embodiments.

[0237] For example, in some embodiments, formatting 1218 the avatar data structure may include formatting the avatar data structure into blocks, where each block contains, e.g., the avatar description elements represented in FIG. 4, and, e.g., includes a header allowing the decoder to unambiguously identify the contents of the block.

[0238] The input files may be a single format or a collection of files in different formats for some embodiments. In the scenario depicted in FIG. 12, which has a collection of multiple file formats, the format analysis 1216 and formatting 1218 of blocks have the objective of fetching (relevant) information and arranging the information in such a way that the original file format may be recovered in either binary or human-readable format. For some embodiments, a header section may be included in an avatar data structure element “packet”, as defined above, to inform the decoder how to decompress and recover the original formats. For some embodiments, the arranging of the information obtained from the input file(s) may be stored in (or populated into) an avatar data structure.

[0239] The JSON output file may include one or more avatar data properties. FIG. 12 shows an example set of avatar data properties. Some JSON output files may include other avatar properties not shown in this example. Furthermore, some JSON output files may use different formats for properties than the formats used in this example. For the example in FIG. 12, the Geometry property may be encoded in “,x3d” format. The Style property may be encoded in “.xml” format. The Animation property may be encoded in “. gltf” format. The Haptics property may be encoded in “.json” format. As a result, the header section or packet may make the same reference to facilitate decoding in a lossless manner back to the original format.

[0240] Format analysis 1216 and formatting 1218 of blocks depicted in FIG. 12 may use parsing mechanisms to extract the (relevant) information in different file formats so that the information may be packed and compressed into a binary compression format or human-readable format.

[0241] The formatting block 1218 allows the generation of a human-readable format instead of a binary format. The human-readable format may be in the form of a JSON file format. For some embodiments, the human-readable format may be a format other than JSON.

[0242] For some embodiments, the binary compression block 1220 applies lossless compression using the normative SPIHT algorithm and Arithmetic Coding (AC) and transforms the data into a bitstream.

[0243] For some embodiments, the binary compression 1220 for “.json” files follow the RFC 8949 Concise Binary Object Representation standard. [0244] For some embodiments, the binary compression 1220 for “,x3d” files follow the ISO/IEC 19776- 3.2:2011 Extensible 3D encodings standard.

[0245] For some embodiments, the binary compression 1220 for “.gltf” files follow the open gITF 2.0 specification from the Khronos group to generate binary “.gib” files.

[0246] For some embodiments, the binary compression 1220 for “.xml” files follow the ISO/IEC 23001 - 1 : 2006 Binary MPEG format for XML

[0247] For some embodiments, packetization 1222 may include organizing binary compression data into packets for each of the elements of a data structure (such as geometry, style, or any of the other elements shown in FIG. 4 and Tables 2-10). For some embodiments, packetization 1222 may be used during transport to request access to part of the data only.

[0248] In one scenario in accordance with some embodiments, both human-readable files (such as JSON files) and binary files may be outputted from an encoder. FIG. 12 presents an example configuration in accordance with some embodiments. In another scenario in accordance with some embodiments, only human-readable files may be outputted from an encoder. In yet another scenario in accordance with some embodiments, only binary files may be outputted from an encoder.

[0249] In some embodiments, human-readable files may provide additional information to complement and/or further explain binary compressed files. In some embodiments, information (e.g., of significant size) might be compressed and transmitted using binary files to reduce the size of the overall transmission.

Decoder

[0250] FIG. 13 is a process diagram illustrating an example decoder architecture according to some embodiments. FIG. 13 illustrates the decoder architecture 1300 for the avatar media codec and properties described above in more detail. The decoder takes a binary file 1302 and/or a set of text files as an input and outputs the original file formats which are interoperable. The input text files may be represented in JSON format file 1204 as shown in FIG. 13, or in any other text file format.

[0251] For the particular file format, the input binary format goes through depacketization 1306 and (binary) decompression 1308 following the same norms used and presented in the encoder section above. The data is extracted from the file and mapped to data structures associated with Tables 2-10. After this step, the application has the information related to the avatar media codec. For some embodiments, a human- readable format may be extracted and mapped back to the original file formats based on additional header information about the properties and the file format from which they were extracted. [0252] For some embodiments, binary decompressed data and/or JSON-format data (or human-readable format data for some embodiments) may be passed through an interchange file format process 1310 and one or more output files 1312 may be outputted. Output file(s) may be in Graphics Library Transmission Format (.gltf) 1314, Extensible Three-Dimensional format (.x3d) 1316, JavaScript Object Notation (Json) 1318, Extensible Markup Language format (.xml) 1320, and/or text format (.txt) format for some embodiments.

[0253] In one scenario in accordance with some embodiments, both human-readable files and binary files may be received by a decoder. FIG. 13 presents an example configuration in accordance with some embodiments. In another scenario in accordance with some embodiments, only human-readable files may be received at a decoder or a decoder may be configured only to receive the human-readable files. In another scenario in accordance with some embodiments, only binary files may be received at a decoder and/or a decoder may be configured only to receive binary files.

[0254] In accordance with some embodiments, the decoder may have binary output in addition to, or instead of, human-readable output.

[0255] FIG. 14 is a flowchart illustrating an example process for encoding avatar data according to some embodiments. For some embodiments, an example process 1400 may include obtaining 1402 an input file, wherein the input file includes avatar content data. For some embodiments, the example process 1400 may further include determining 1404 a data type corresponding to the avatar content data. For some embodiments, the example process 1400 may further include processing 1406 the avatar data to extract information from the input file based on the data type and codec type. For some embodiments, the example process 1400 may further include inferring 1408 template-based information regarding the avatar content data. For some embodiments, the example process 1400 may further include formatting 1410 the avatar content data based on a profile type and the template-based information. For some embodiments, the example process 1400 may further include encoding 1412 the formatted avatar data.

[0256] FIG. 15 is a flowchart illustrating an example process for decoding avatar data according to some embodiments. For some embodiments, an example process 1500 may include obtaining 1502 encoded avatar data. For some embodiments, the example process 1500 may further include decoding 1504 the encoded avatar data to generate decoded avatar data. For some embodiments, the example process 1500 may further include determining 1506 a data type corresponding to the decoded avatar data. For some embodiments, the example process 1500 may further include processing 1508 the decoded avatar data based on the data type and codec type. For some embodiments, the example process 1500 may further include rendering 1510 the processed and decoded avatar data. [0257] While the methods and systems in accordance with some embodiments are generally discussed in context of extended reality (XR), some embodiments may be applied to any XR contexts such as, e.g., virtual reality (VR) / mixed reality (MR) / augmented reality (AR) contexts. Also, although the term “head mounted display (HMD)” is used herein in accordance with some embodiments, some embodiments may be applied to a wearable device (which may or may not be attached to the head) capable of, e.g., XR, VR, AR, and/or MR for some embodiments.

[0258] A first example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a computer generated (CG) profile type; and encoding the formatted avatar data.

[0259] In some embodiments of the first example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0260] In some embodiments of the first example method, the codec type is one of a codec set including: VPCC, GPCC, V-DMC, gITF, and USD.

[0261] In some embodiments of the first example method, the formatted avatar data includes avatar animation data.

[0262] In some embodiments of the first example method, the formatted avatar data includes avatar geometry data.

[0263] In some embodiments of the first example method, the formatted avatar data includes avatar appearance data.

[0264] A first example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above.

[0265] A second example method in accordance with some embodiments may include: obtaining encoded avatar data; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type, wherein the profile type is a computer generated (CG) profile type; and rendering the processed and decoded avatar data. [0266] In some embodiments of the second example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0267] In some embodiments of the second example method, the codec type is one of a codec set including: VPCC, GPCC, V-DMC, gITF, and USD.

[0268] In some embodiments of the second example method, the decoded avatar data includes avatar animation data.

[0269] In some embodiments of the second example method, the decoded avatar data includes avatar geometry data.

[0270] In some embodiments of the second example method, the decoded avatar data includes avatar appearance data.

[0271] A second example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above.

[0272] A third example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a parametric profile type; and encoding the formatted avatar data.

[0273] In some embodiments of the third example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0274] In some embodiments of the third example method, the formatted avatar data includes weights for a parameter.

[0275] In some embodiments of the third example method, the parameter is one of a property set including: animation, shape, texture, and pose properties.

[0276] In some embodiments of the third example method, the formatted avatar data includes a parameter-based model of an avatar.

[0277] A third example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above. [0278] A fourth example method in accordance with some embodiments may include: obtaining encoded avatar data; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type, wherein the profile type is a parametric profile type; and rendering the processed and decoded avatar data.

[0279] In some embodiments of the fourth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0280] In some embodiments of the fourth example method, the decoded avatar data includes weights for a parameter.

[0281] In some embodiments of the fourth example method, the parameter is one of a property set including: animation, shape, texture, and pose properties.

[0282] In some embodiments of the fourth example method, the decoded avatar data includes a parameter-based model of an avatar.

[0283] A fourth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above.

[0284] A fifth example method in accordance with some embodiments may include: obtaining an input file, wherein the input file includes avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is an artificial intelligence (Al) profile type; and encoding the formatted avatar data.

[0285] In some embodiments of the fifth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0286] In some embodiments of the fifth example method, the formatted avatar data includes information corresponding to a neural network.

[0287] In some embodiments of the fifth example method, encoding the formatted avatar data includes using a neural network. [0288] A fifth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above.

[0289] A sixth example method in accordance with some embodiments may include: obtaining encoded avatar data; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type, wherein the profile type is an artificial intelligence (Al) profile type; and rendering the processed and decoded avatar data.

[0290] In some embodiments of the sixth example method, the data type is one of a data set including: video data, image data, mesh data, and game data.

[0291] In some embodiments of the sixth example method, the decoded avatar data includes information corresponding to a neural network.

[0292] In some embodiments of the sixth example method, decoding the encoded avatar data includes using a neural network.

[0293] A sixth example apparatus in accordance with some embodiments may include: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform any of the methods listed above.

[0294] A seventh example apparatus in accordance with some embodiments may include at least one processor configured to perform any one of the methods listed above.

[0295] An eighth example apparatus in accordance with some embodiments may include a computer- readable medium storing instructions for causing one or more processors to perform any one of the methods listed above.

[0296] A ninth example apparatus in accordance with some embodiments may include at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform any one of the methods listed above.

[0297] An example signal in accordance with some embodiments may include encoded and formatted avatar data generated according to any one of the methods listed above.

[0298] This disclosure describes a variety of aspects, including tools, features, embodiments, models, approaches, etc. Many of these aspects are described with specificity and, at least to show the individual characteristics, are often described in a manner that may sound limiting. However, this is for purposes of clarity in description, and does not limit the disclosure or scope of those aspects. Indeed, all of the different aspects can be combined and interchanged to provide further aspects. Moreover, the aspects can be combined and interchanged with aspects described in earlier filings as well.

[0299] The aspects described and contemplated in this disclosure can be implemented in many different forms. While some embodiments are illustrated specifically, other embodiments are contemplated, and the discussion of particular embodiments does not limit the breadth of the implementations. At least one of the aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to transmitting a bitstream generated or encoded. These and other aspects can be implemented as a method, an apparatus, a computer readable storage medium having stored thereon instructions for encoding or decoding video data according to any of the methods described, and/or a computer readable storage medium having stored thereon a bitstream generated according to any of the methods described.

[0300] In the present disclosure, the terms “reconstructed” and “decoded” may be used interchangeably, the terms “pixel” and “sample” may be used interchangeably, the terms “image,” “picture” and “frame” may be used interchangeably. Usually, but not necessarily, the term “reconstructed” is used at the encoder side while “decoded” is used at the decoder side.

[0301] The terms HDR (high dynamic range) and SDR (standard dynamic range) often convey specific values of dynamic range to those of ordinary skill in the art. However, additional embodiments are also intended in which a reference to HDR is understood to mean “higher dynamic range” and a reference to SDR is understood to mean “lower dynamic range.” Such additional embodiments are not constrained by any specific values of dynamic range that might often be associated with the terms “high dynamic range” and “standard dynamic range.”

[0302] Various methods are described herein, and each of the methods comprises one or more steps or actions for achieving the described method. Unless a specific order of steps or actions is required for proper operation of the method, the order and/or use of specific steps and/or actions may be modified or combined. Additionally, terms such as “first”, “second”, etc. may be used in various embodiments to modify an element, component, step, operation, etc., such as, for example, a “first decoding” and a “second decoding”. Use of such terms does not imply an ordering to the modified operations unless specifically required. So, in this example, the first decoding need not be performed before the second decoding, and may occur, for example, before, during, or in an overlapping time period with the second decoding.

[0303] Various numeric values may be used in the present disclosure, for example. The specific values are for example purposes and the aspects described are not limited to these specific values. [0304] Embodiments described herein may be carried out by computer software implemented by a processor or other hardware, or by a combination of hardware and software. As a non-limiting example, the embodiments can be implemented by one or more integrated circuits. The processor can be of any type appropriate to the technical environment and can encompass one or more of microprocessors, general purpose computers, special purpose computers, and processors based on a multi-core architecture, as nonlimiting examples.

[0305] Various implementations involve decoding. “Decoding”, as used in this disclosure, can encompass all or part of the processes performed, for example, on a received encoded sequence in order to produce a final output suitable for display. In various embodiments, such processes include one or more of the processes typically performed by a decoder, for example, entropy decoding, inverse quantization, inverse transformation, and differential decoding. In various embodiments, such processes also, or alternatively, include processes performed by a decoder of various implementations described in this disclosure, for example, extracting a picture from a tiled (packed) picture, determining an upsampling filter to use and then upsampling a picture, and flipping a picture back to its intended orientation.

[0306] As further examples, in one embodiment “decoding” refers only to entropy decoding, in another embodiment “decoding” refers only to differential decoding, and in another embodiment “decoding” refers to a combination of entropy decoding and differential decoding. Whether the phrase “decoding process” is intended to refer specifically to a subset of operations or generally to the broader decoding process will be clear based on the context of the specific descriptions.

[0307] Various implementations involve encoding. In an analogous way to the above discussion about “decoding”, “encoding” as used in this disclosure can encompass all or part of the processes performed, for example, on an input video sequence in order to produce an encoded bitstream. In various embodiments, such processes include one or more of the processes typically performed by an encoder, for example, partitioning, differential encoding, transformation, quantization, and entropy encoding. In various embodiments, such processes also, or alternatively, include processes performed by an encoder of various implementations described in this disclosure.

[0308] As further examples, in one embodiment “encoding” refers only to entropy encoding, in another embodiment “encoding” refers only to differential encoding, and in another embodiment “encoding” refers to a combination of differential encoding and entropy encoding. Whether the phrase “encoding process” is intended to refer specifically to a subset of operations or generally to the broader encoding process will be clear based on the context of the specific descriptions. [0309] When a figure is presented as a flow diagram, it should be understood that it also provides a block diagram of a corresponding apparatus. Similarly, when a figure is presented as a block diagram, it should be understood that it also provides a flow diagram of a corresponding method/process.

[0310] Various embodiments refer to rate distortion optimization. In particular, during the encoding process, the balance or trade-off between the rate and distortion is usually considered, often given the constraints of computational complexity. The rate distortion optimization is usually formulated as minimizing a rate distortion function, which is a weighted sum of the rate and of the distortion. There are different approaches to solve the rate distortion optimization problem. For example, the approaches may be based on an extensive testing of all encoding options, including all considered modes or coding parameters values, with a complete evaluation of their coding cost and related distortion of the reconstructed signal after coding and decoding. Faster approaches may also be used, to save encoding complexity, in particular with computation of an approximated distortion based on the prediction or the prediction residual signal, not the reconstructed one. A mix of these two approaches can also be used, such as by using an approximated distortion for only some of the possible encoding options, and a complete distortion for other encoding options. Other approaches only evaluate a subset of the possible encoding options. More generally, many approaches employ any of a variety of techniques to perform the optimization, but the optimization is not necessarily a complete evaluation of both the coding cost and related distortion.

[0311] The implementations and aspects described herein can be implemented in, for example, a method or a process, an apparatus, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of implementation (for example, discussed only as a method), the implementation of features discussed can also be implemented in other forms (for example, an apparatus or program). An apparatus can be implemented in, for example, appropriate hardware, software, and firmware. The methods can be implemented in, for example, a processor, which refers to processing devices in general, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. Processors also include communication devices, such as, for example, computers, cell phones, portable/personal digital assistants (“PDAs”), and other devices that facilitate communication of information between end-users.

[0312] Reference to “one embodiment” or “an embodiment” or “one implementation” or “an implementation”, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment” or “in one implementation” or “in an implementation”, as well any other variations, appearing in various places throughout this disclosure are not necessarily all referring to the same embodiment. [0313] Additionally, this disclosure may refer to “determining” various pieces of information. Determining the information can include one or more of, for example, estimating the information, calculating the information, predicting the information, or retrieving the information from memory.

[0314] Further, this disclosure may refer to “accessing” various pieces of information. Accessing the information can include one or more of, for example, receiving the information, retrieving the information (for example, from memory), storing the information, moving the information, copying the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0315] Additionally, this disclosure may refer to “receiving” various pieces of information. Receiving is, as with “accessing”, intended to be a broad term. Receiving the information can include one or more of, for example, accessing the information, or retrieving the information (for example, from memory). Further, “receiving” is typically involved, in one way or another, during operations such as, for example, storing the information, processing the information, transmitting the information, moving the information, copying the information, erasing the information, calculating the information, determining the information, predicting the information, or estimating the information.

[0316] It is to be appreciated that the use of any of the following 7”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This may be extended for as many items as are listed.

[0317] Also, as used herein, the word “signal” refers to, among other things, indicating something to a corresponding decoder. For example, in certain embodiments the encoder signals a particular one of a plurality of parameters for region-based filter parameter selection for de-artifact filtering. In this way, in an embodiment the same parameter is used at both the encoder side and the decoder side. Thus, for example, an encoder can transmit (explicit signaling) a particular parameter to the decoder so that the decoder can use the same particular parameter. Conversely, if the decoder already has the particular parameter as well as others, then signaling can be used without transmitting (implicit signaling) to simply allow the decoder to know and select the particular parameter. By avoiding transmission of any actual functions, a bit savings is realized in various embodiments. It is to be appreciated that signaling can be accomplished in a variety of ways. For example, one or more syntax elements, flags, and so forth are used to signal information to a corresponding decoder in various embodiments. While the preceding relates to the verb form of the word “signal”, the word “signal” can also be used herein as a noun.

[0318] Implementations can produce a variety of signals formatted to carry information that can be, for example, stored or transmitted. The information can include, for example, instructions for performing a method, or data produced by one of the described implementations. For example, a signal can be formatted to carry the bitstream of a described embodiment. Such a signal can be formatted, for example, as an electromagnetic wave (for example, using a radio frequency portion of spectrum) or as a baseband signal. The formatting can include, for example, encoding a data stream and modulating a carrier with the encoded data stream. The information that the signal carries can be, for example, analog or digital information. The signal can be transmitted over a variety of different wired or wireless links, as is known. The signal can be stored on a processor-readable medium.

[0319] We describe a number of embodiments. Features of these embodiments can be provided alone or in any combination, across various claim categories and types. Further, embodiments can include one or more of the following features, devices, or aspects, alone or in any combination, across various claim categories and types:

• Adapting residues at an encoder according to any of the embodiments discussed.

• A bitstream or signal that includes one or more of the described syntax elements, or variations thereof.

• A bitstream or signal that includes syntax conveying information generated according to any of the embodiments described.

• Inserting in the signaling syntax elements that enable the decoder to adapt residues in a manner corresponding to that used by an encoder.

• Creating and/or transmitting and/or receiving and/or decoding a bitstream or signal that includes one or more of the described syntax elements, or variations thereof.

• Creating and/or transmitting and/or receiving and/or decoding according to any of the embodiments described.

• A method, process, apparatus, medium storing instructions, medium storing data, or signal according to any of the embodiments described.

• A TV, set-top box, cell phone, tablet, or other electronic device that performs adaptation of filter parameters according to any of the embodiments described. • A TV, set-top box, cell phone, tablet, or other electronic device that performs adaptation of filter parameters according to any of the embodiments described, and that displays (e.g. using a monitor, screen, or other type of display) a resulting image.

• A TV, set-top box, cell phone, tablet, or other electronic device that selects (e.g. using a tuner) a channel to receive a signal including an encoded image, and performs adaptation of filter parameters according to any of the embodiments described.

• A TV, set-top box, cell phone, tablet, or other electronic device that receives (e.g. using an antenna) a signal over the air that includes an encoded image, and performs adaptation of filter parameters according to any of the embodiments described.

[0320] Note that various hardware elements of one or more of the described embodiments are referred to as “modules” that carry out (i.e., perform, execute, and the like) various functions that are described herein in connection with the respective modules. As used herein, a module includes hardware (e.g., one or more processors, one or more microprocessors, one or more microcontrollers, one or more microchips, one or more application-specific integrated circuits (ASICs), one or more field programmable gate arrays (FPGAs), one or more memory devices) deemed suitable by those of skill in the relevant art for a given implementation. Each described module may also include instructions executable for carrying out the one or more functions described as being carried out by the respective module, and it is noted that those instructions could take the form of or include hardware (i.e., hardwired) instructions, firmware instructions, software instructions, and/or the like, and may be stored in any suitable non-transitory computer-readable medium or media, such as commonly referred to as RAM, ROM, etc.

[0321] Although features and elements are described above in particular combinations, one of ordinary skill in the art will appreciate that each feature or element can be used alone or in any combination with the other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable storage media include, but are not limited to, a read only memory (ROM), a random access memory (RAM), a register, cache memory, semiconductor memory devices, magnetic media such as internal hard disks and removable disks, magneto-optical media, and optical media such as CD-ROM disks, and digital versatile disks (DVDs). A processor in association with software may be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A method comprising: obtaining an input file, wherein the input file comprises avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a computer generated (CG) profile type; and encoding the formatted avatar data.

2. The method of claim 1 , wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

3. The method of any one of claims 1-2, wherein the codec type is one of a codec set comprising: VPCC,

GPCC, V-DMC, gITF, and USD.

4. The method of any one of claims 1-3, wherein the formatted avatar data comprises avatar animation data.

5. The method of any one of claims 1 -3, wherein the formatted avatar data comprises avatar geometry data.

6. The method of any one of claims 1-3, wherein the formatted avatar data comprises avatar appearance data.

7. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 1 through 6.

8. A method comprising: obtaining encoded avatar data, wherein the encoded avatar data is associated with a computer generated (CG) profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

9. The method of claim 8, wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

10. The method of any one of claims 8-9, wherein the codec type is one of a codec set comprising: VPCC,

GPCC, V-DMC, gITF, and USD.

11 . The method of any one of claims 8-10, wherein the decoded avatar data comprises avatar animation data.

12. The method of any one of claims 8-10, wherein the decoded avatar data comprises avatar geometry data.

13. The method of any one of claims 8-10, wherein the decoded avatar data comprises avatar appearance data.

14. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 8 through 13.

15. A method comprising: obtaining an input file, wherein the input file comprises avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is a parametric profile type; and encoding the formatted avatar data.

16. The method of claim 15, wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

17. The method of any one of claims 15-16, wherein the formatted avatar data comprises weights for a parameter.

18. The method of claim 17, wherein the parameter is one of a property set comprising: animation, shape, texture, and pose properties.

19. The method of any one of claims 15-18, wherein the formatted avatar data comprises a parameter-based model of an avatar.

20. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 15 through 19.

21. A method comprising: obtaining encoded avatar data, wherein the encoded avatar data is associated with a parametric profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

22. The method of claim 21, wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

23. The method of any one of claims 21-22, wherein the decoded avatar data comprises weights for a parameter.

24. The method of claim 23, wherein the parameter is one of a property set comprising: animation, shape, texture, and pose properties.

25. The method of any one of claims 21-24, wherein the decoded avatar data comprises a parameter-based model of an avatar.

26. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 21 through 25.

27. A method comprising: obtaining an input file, wherein the input file comprises avatar content data; determining a data type corresponding to the avatar content data; processing the avatar data to extract information from the input file based on the data type and codec type; inferring template-based information regarding the avatar content data; formatting the avatar content data based on a profile type and the template-based information, wherein the profile type is an artificial intelligence (Al) profile type; and encoding the formatted avatar data.

28. The method of claim 27, wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

29. The method of any one of claims 27-28, wherein the formatted avatar data comprises information corresponding to a neural network.

30. The method of any one of claims 27-29, wherein encoding the formatted avatar data comprises using a neural network.

31. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 27 through 30.

32. A method comprising: obtaining encoded avatar data, wherein the encoded avatar data is associated with an artificial intelligence (Al) profile type; decoding the encoded avatar data to generate decoded avatar data; determining a data type corresponding to the decoded avatar data; processing the decoded avatar data based on the data type and codec type; and rendering the processed and decoded avatar data.

33. The method of claim 32, wherein the data type is one of a data set comprising: video data, image data, mesh data, and game data.

34. The method of any one of claims 32-33, wherein the decoded avatar data comprises information corresponding to a neural network.

35. The method of any one of claims 32-34, wherein decoding the encoded avatar data comprises using a neural network.

36. An apparatus comprising: a processor; and a non-transitory computer-readable medium storing instructions operative, when executed by the processor, to cause the apparatus to perform the method of any one of claims 32 through 35.

37. An apparatus comprising at least one processor configured to perform the method of any one of claims

1-6, 8-13, 15-19, 21 -25, 27-30, and 32-35.

38. An apparatus comprising a computer-readable medium storing instructions for causing one or more processors to perform the method of any one of claims 1-6, 8-13, 15-19, 21 -25, 27-30, and 32-35.

39. An apparatus comprising at least one processor and at least one non-transitory computer-readable medium storing instructions for causing the at least one processor to perform the method of any one of claims 1-6, 8-13, 15-19, 21-25, 27-30, and 32-35.

40. A signal including encoded and formatted avatar data generated according to any one of claims 1 -6, 15- 19, and 27-30.