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US20040239675A1 - Method of rendering an image and a method of animating a graphics character - Google Patents

Method of rendering an image and a method of animating a graphics character Download PDF

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Publication number
US20040239675A1
US20040239675A1 US10/486,348 US48634804A US2004239675A1 US 20040239675 A1 US20040239675 A1 US 20040239675A1 US 48634804 A US48634804 A US 48634804A US 2004239675 A1 US2004239675 A1 US 2004239675A1
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Prior art keywords
image
character
images
fuzzy
rendering
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US10/486,348
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English (en)
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Stephen Regelous
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Individual
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Assigned to REGELOUS, STEPHEN JOHN, REGELOUS, STEPHEN NOEL reassignment REGELOUS, STEPHEN JOHN ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: REGELOUS, STEPHEN JOHN
Publication of US20040239675A1 publication Critical patent/US20040239675A1/en
Priority to US12/453,674 priority Critical patent/US7697004B2/en
Abandoned legal-status Critical Current

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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T13/00—Animation
    • G06T13/20—3D [Three Dimensional] animation
    • G06T13/40—3D [Three Dimensional] animation of characters, e.g. humans, animals or virtual beings

Definitions

  • the present invention relates to a method of rendering an image and a method of animating a graphics character in response to rendered image information. More particularly, but not exclusively, the present invention relates to a method of rendering an image viewed by an autonomous computer generated character and a method for controlling behaviour of the character based upon the rendered image.
  • the present invention may find particular application in autonomous computer generated character animation for video games and to produce visual effects for film or video. The invention may also find application in simulation and robot navigation applications.
  • topological features such as holes, concavities and profile are difficult and inefficient to represent in a non vision based system.
  • a disadvantage of such known systems is characters are unable to be realistically responsive to their entire environment represented as a spherical view from the character's position.
  • the known systems process the image analytically to identify characteristics about objects within the field of view.
  • a disadvantage of such systems is that this processing costs computation time.
  • a method of generating behaviour of a graphics character within an environment including a graphics character and one or more graphics element including the steps of:
  • Image data is preferably obtained by one of the rendering methods described below.
  • Image data for the view generated is preferably processed within the first layer of an artificial intelligence engine to obtain fuzzy logic values.
  • a second layer of the artificial intelligence engine may apply fuzzy rules to the fuzzy logic values to obtain input values for a middle layer of the artificial intelligence engine.
  • the second layer of the artificial intelligence engine may be a neural network.
  • These values may be processed by the middle layer of the artificial intelligence engine to obtain activation values to control character behaviour.
  • the middle layer of the artificial intelligence engine may be a fuzzy logic network, a neural network or a binary logic network. Where the middle layer is a fuzzy logic network a defuzzifying output layer may be employed.
  • Sound registers may also be associated with the character. Behaviour of the character may be based upon image and/or sound information.
  • the images rendered are preferably derived from a plurality of adjacent image subspace sectors and combined to form a single image.
  • a scanline rendering technique is preferably employed.
  • Image subspace areas may be vertically and/or horizontally subdivided.
  • the sub images are preferably in the form of a rectangular grid.
  • the image formed is preferably rendered in Cartesian space utilising the two polar axes.
  • FIGS. 1 and 2 illustrate a first rendering method
  • FIGS. 3 and 4 illustrate a second rendering method.
  • FIGS. 5 to 7 illustrate the rendering of a sub image component.
  • FIG. 8 illustrates an image rendered to guide an autonomous computer generated character
  • FIG. 9 shows an example of an artificial intelligence engine utilised to control an autonomous computer generated character.
  • FIG. 10 shows an example of an artificial intelligence engine including a neural network as a layer.
  • the present invention discloses a method of determining the behaviour of an autonomous computer generated character based upon a generated image of the graphical environment around the character rendered from the viewing perspective of the character. This approach simplifies computation and enables realistic character behaviour to be achieved relatively easily.
  • image refers to an array of pixels in which each pixel includes at least one data bit.
  • an image typically includes multiple planes of pixel data (i.e. each pixel includes multiple attributes such as luminance, colour, distance from point of view, relative velocity etc.).
  • the first step is to render an image of the environment as viewed from the perspective of the character for which behaviour is being generated. Where a relatively narrow range of view (e.g. 90°) is utilised to determine character behaviour, standard scanline rendering approaches may be employed. Ray tracing methods could be employed, although they would suffer from a much higher computational overhead.
  • a relatively narrow range of view e.g. 90°
  • standard scanline rendering approaches may be employed. Ray tracing methods could be employed, although they would suffer from a much higher computational overhead.
  • a 180° viewing area is divided into three sectors 1 , 2 and 3 . It will be appreciated that any desired viewing range up to 360° may be selected.
  • the sectors define fields of view (in this case 60° segments) from the perspective of an autonomous computer generated character 4 .
  • the environment consists of blocks 5 to 9 .
  • an image is generated utilising a conventional scanline rendering process for each section 1 , 2 and 3 .
  • the three images rendered are merged together to produce a single image as shown in FIG. 2.
  • the resulting bit mapped image shown in FIG. 2 is a simulated scene of the environment as viewed from the character's point of view and may be utilised as the input to an artificial intelligence engine.
  • Each pixel may have a number of attributes.
  • FIGS. 3 to 7 a second rendering method utilising a polar viewing transform will be described.
  • FIG. 3 shows a plan view of an environment consisting of blocks 5 to 9 surrounding an autonomous computer generated character 4 . Dashed lines are shown extending from the point of view of character 4 to the vertices of environmental objects 5 to 9 .
  • FIG. 5 shows the cartesian co-ordinates of the vertices of block 7 as ⁇ 100, 0, 100 and 100, 0, 100.
  • the vertices are shown at ⁇ 45° and +45° (with z values of 141 ).
  • a polar space interpolation has been effected in FIG. 6 between the vertices resulting in a square primitive.
  • the x and y axes are polar angles and the z value is an attribute of each pixel.
  • graphics primitive 7 may be projected into sub images 12 a and 12 b .
  • the number of sub images employed will depend upon the resolution required.
  • Graphics primitive 7 is clipped against sub images 12 a and 12 b to form clipped primitives 7 a and 7 b .
  • the intersections between the edges of sub regions 12 a and 12 b and the edges of graphics primitive 7 define new vertices (e.g. 0 , 0 , 100 ).
  • Polar transformations are performed on the vertices of the clipped images and by interpolating across the surface of the primitive polar sub images are formed.
  • the polar sub images may then be rendered into the main image using linear interpolation scanline algorithms to produce an image in pseudo polar space as shown in FIG. 7.
  • the image rendered in FIG. 7 is seen to be much closer to a true polar image than the original shown in FIG. 6.
  • FIG. 4 shows a relatively finely rendered image produced using this technique. As the rendered image is simply being used to control character behaviour, relatively coarse image rendering may be acceptable in most situations.
  • An articulated autonomous computer generated character 13 is shown within a graphics environment.
  • An image 14 may be generated from the character's point of view utilising a rendering method as described above or another suitable rendering method.
  • Image 14 may be rendered to a resolution appropriate for the behaviour to be modelled.
  • Behaviour of character 13 may be based upon rendered image 14 and/or sound information.
  • An image from the perspective of an autonomous computer generated character, such as image 14 may be input to the first layer.
  • the image is input to the first layer on a pixel-by-pixel basis.
  • the first layer applies fuzzy membership functions to pixel values to obtain fuzzy weightings.
  • two pixel values are utilised.
  • “Vision X” represents a pixel's horizontal co-ordinate value.
  • “Vision Z” represents a pixel's depth value. Fuzzy weightings may be accorded to the pixel's leftness 17 , centreness 18 , and rightness 19 . Fuzzy weighting may also be accorded to the pixel's very nearness 20 , nearness 21 , and farness 22 .
  • the pixels of image 14 may be processed in parallel or as a serial iterated loop to provide fuzzy values for each pixel.
  • a fuzzy parallel processing layer 23 is provided to apply fuzzy rules to the output values of fuzzy membership functions 17 to 22 on a pixel-by-pixel basis.
  • the values output from each rule in parallel processing layer 23 may then be averaged, a maximum value adopted or some other function applied to obtain a single output value from parallel processing layer 23 for each rule.
  • a,fuzzy near left rule utilises the fuzzy weightings from the leftness of the pixel and the very nearness of the pixel to output a value.
  • Fuzzy parallel processing layer 23 may be replaced by a neural network.
  • middle layer 32 The single values from each rule of layer 23 are then utilised by middle layer 32 .
  • middle layer 32 fuzzy rules are applied to develop activation values.
  • the middle layer applies a fuzzy “near left and right then go right” rule to inputs from fuzzy rules near left and near right from layer 23 .
  • Middle layer 32 may be replaced by a neural network, binary logic network etc.
  • fuzzy parallel processing layer 23 and middle layer 32 may be replaced by a single neural network.
  • Values from the middle layer 32 are then defuzzified in layer 24 to provide activation values (behavioural outputs).
  • the outputs of fuzzy middle layer 32 are utilised to determine the weighting of a “stop” value 25 .
  • “Go” value 26 is the complement of “stop” value 25 .
  • the values 25 and 26 determine the “z” speed value 27 .
  • the outputs from fuzzy middle layer 32 are also utilised to control turning.
  • the middle layer values determine the values of “right” value 28 , “no turn” value 29 and “left” value 30 . These are utilised to obtain a “RY” turn value 31 .
  • FIG. 10 an example of an artificial intelligence engine wherein one of the layers is a neural network is shown.
  • An image from the perspective of an autonomous computer generated character, such as image 14 may be input on a pixel-by-pixel basis to the artificial intelligence engine.
  • Each pixel is split into its constituent parts 33 and provided to fuzzy processing layer 34 wherein fuzzy membership functions are applied to obtain fuzzy weightings.
  • a neural network comprised of an input sub-layer 35 , a first hidden sub-layer 36 , a second hidden sub-layer 37 , and an output sub-layer 38 is shown.
  • the fuzzy weightings output from fuzzy processing layer 34 are provided to the input sub-layer 35 .
  • a neural network utilising perceptrons is shown. It will be appreciated that other forms of neural networks may be used.
  • the output values 39 from the neural network provide activation values 40 .
  • the behaviour of character 13 may also be dependent upon sound information. Where a sound source is defined within the environment a calculation is first conducted to determine whether the character is within audible range. If so the amplitude of this sound at the location of the character is calculated. If there is an empty sound register the sound value is placed in that register. If there is no empty register the lowest amplitude value within an existing register is replaced if lower than the value of the propagated sound. The register may contain information as to the amplitude, frequency or other characteristics. Words could also, be stored and character behaviour may respond to audible words. Information from the sound registers may be fed into middle layer 32 to control behaviour of character 13 . Sound information is thus based upon a real physical model in the same way visual information is.
  • the artificial intelligence engine may respond to any desired inputs, such as colour, brightness, contrast, distance from point of view, relative velocity, sound etc. It will also be appreciated that a range of behaviours may be generated, such as obstacle avoidance, navigation, interest tracking, multiple character interaction or the propagation of sound.
  • the output of the artificial intelligence engine may also control animation blending controls. Animation blending controls may be utilised to initiate play back of animation, blend values to control the relative effect of multiple animations, control animation play back rates etc.
  • the method of the invention produces surprisingly natural behaviour utilising only a few simple fuzzy rules. Realistic behaviour can be more easily achieved due to the realistic nature of the input data.
  • the method avoids the need to explicitly compute occlusion, visibility and sensory resolution. These are all implicit within the rendering method. Better than n squared scalability for processing times of large numbers of characters in a scene are obtainable (typically close to n proportional where n is the number of characters).
  • the invention thus provides means to animate an autonomous computer generated character in a realistic manner without high computation requirements.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Processing Or Creating Images (AREA)
US10/486,348 2001-08-10 2002-08-07 Method of rendering an image and a method of animating a graphics character Abandoned US20040239675A1 (en)

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US12/453,674 US7697004B2 (en) 2001-08-10 2009-05-18 Method of animating a graphics character using fuzzy processing layers

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NZ51350401 2001-08-10
NZ513504 2001-08-10
PCT/NZ2002/000151 WO2003015034A1 (fr) 2001-08-10 2002-08-07 Procede permettant de restituer une image et procede permettant d'animer un caractere graphique

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070011666A1 (en) * 2005-07-08 2007-01-11 Microsoft Corporation Selective pre-compilation of virtual code to enhance emulator performance
US20070097139A1 (en) * 2005-11-02 2007-05-03 Chao-Chin Chen Method and apparatus of primitive filter in graphic process applications
US20090187529A1 (en) * 2005-02-25 2009-07-23 Stephen John Regelous Method of Generating Behavior for a Graphics Character and Robotics Devices

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8200594B1 (en) * 2008-09-10 2012-06-12 Nvidia Corporation System, method, and computer program product for accelerating a game artificial intelligence process
US10460427B2 (en) * 2017-11-22 2019-10-29 The Government Of The United States Of America, As Represented By The Secretary Of The Navy Converting imagery and charts to polar projection

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5268998A (en) * 1990-11-27 1993-12-07 Paraspectives, Inc. System for imaging objects in alternative geometries
US5345541A (en) * 1991-12-20 1994-09-06 Apple Computer, Inc. Method and apparatus for approximating a value between two endpoint values in a three-dimensional image rendering device
US5436839A (en) * 1992-10-26 1995-07-25 Martin Marietta Corporation Navigation module for a semi-autonomous vehicle
US5561756A (en) * 1992-05-08 1996-10-01 Apple Computer, Inc. Textured sphere and spherical environment map rendering using texture map double indirection
US5864342A (en) * 1995-08-04 1999-01-26 Microsoft Corporation Method and system for rendering graphical objects to image chunks
US5923337A (en) * 1996-04-23 1999-07-13 Image Link Co., Ltd. Systems and methods for communicating through computer animated images
US6064388A (en) * 1997-11-10 2000-05-16 Cognex Corporation Cartesian to polar coordinate transformation
US6139434A (en) * 1996-09-24 2000-10-31 Nintendo Co., Ltd. Three-dimensional image processing apparatus with enhanced automatic and user point of view control
US6282526B1 (en) * 1999-01-20 2001-08-28 The United States Of America As Represented By The Secretary Of The Navy Fuzzy logic based system and method for information processing with uncertain input data
US6446056B1 (en) * 1999-09-10 2002-09-03 Yamaha Hatsudoki Kabushiki Kaisha Interactive artificial intelligence
US6614917B1 (en) * 1999-10-22 2003-09-02 Lockheed Martin Corporation Dynamic process for identifying objects in multi-dimensional data

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE69627154D1 (de) * 1996-10-17 2003-05-08 St Microelectronics Srl Modular aufgebautes Fuzzy-Logik neuronales Netzwerk
GB2346526B (en) * 1997-07-25 2001-02-14 Motorola Inc Method and apparatus for providing virtual actors using neural network and text-to-linguistics
US6819780B2 (en) * 2001-02-02 2004-11-16 Cnh America Llc Method and apparatus for automatically steering a vehicle in an agricultural field using a plurality of fuzzy logic membership functions

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5268998A (en) * 1990-11-27 1993-12-07 Paraspectives, Inc. System for imaging objects in alternative geometries
US5345541A (en) * 1991-12-20 1994-09-06 Apple Computer, Inc. Method and apparatus for approximating a value between two endpoint values in a three-dimensional image rendering device
US5561756A (en) * 1992-05-08 1996-10-01 Apple Computer, Inc. Textured sphere and spherical environment map rendering using texture map double indirection
US5436839A (en) * 1992-10-26 1995-07-25 Martin Marietta Corporation Navigation module for a semi-autonomous vehicle
US5864342A (en) * 1995-08-04 1999-01-26 Microsoft Corporation Method and system for rendering graphical objects to image chunks
US5923337A (en) * 1996-04-23 1999-07-13 Image Link Co., Ltd. Systems and methods for communicating through computer animated images
US6139434A (en) * 1996-09-24 2000-10-31 Nintendo Co., Ltd. Three-dimensional image processing apparatus with enhanced automatic and user point of view control
US6064388A (en) * 1997-11-10 2000-05-16 Cognex Corporation Cartesian to polar coordinate transformation
US6282526B1 (en) * 1999-01-20 2001-08-28 The United States Of America As Represented By The Secretary Of The Navy Fuzzy logic based system and method for information processing with uncertain input data
US6446056B1 (en) * 1999-09-10 2002-09-03 Yamaha Hatsudoki Kabushiki Kaisha Interactive artificial intelligence
US6614917B1 (en) * 1999-10-22 2003-09-02 Lockheed Martin Corporation Dynamic process for identifying objects in multi-dimensional data

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090187529A1 (en) * 2005-02-25 2009-07-23 Stephen John Regelous Method of Generating Behavior for a Graphics Character and Robotics Devices
US20070011666A1 (en) * 2005-07-08 2007-01-11 Microsoft Corporation Selective pre-compilation of virtual code to enhance emulator performance
US7389500B2 (en) 2005-07-08 2008-06-17 Microsoft Corporation Selective pre-compilation of virtual code to enhance boot time emulator performance
US20070097139A1 (en) * 2005-11-02 2007-05-03 Chao-Chin Chen Method and apparatus of primitive filter in graphic process applications

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WO2003015034A1 (fr) 2003-02-20
US20090284533A1 (en) 2009-11-19
US7697004B2 (en) 2010-04-13
CA2456835A1 (fr) 2003-02-20
CA2456835C (fr) 2012-03-20

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Owner name: REGELOUS, STEPHEN JOHN, NEW ZEALAND

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:REGELOUS, STEPHEN JOHN;REEL/FRAME:016355/0942

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Owner name: REGELOUS, STEPHEN NOEL, NEW ZEALAND

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