| A deep learning framework for character motion synthesis and editing D Holden, J Saito, T Komura ACM Transactions on Graphics (ToG) 35 (4), 1-11, 2016 | 885 | 2016 |
| Phase-functioned neural networks for character control D Holden, T Komura, J Saito ACM Transactions on Graphics (TOG) 36 (4), 1-13, 2017 | 775 | 2017 |
| Learning motion manifolds with convolutional autoencoders D Holden, J Saito, T Komura, T Joyce SIGGRAPH Asia 2015 technical briefs, 1-4, 2015 | 404 | 2015 |
| DReCon: data-driven responsive control of physics-based characters K Bergamin, S Clavet, D Holden, JR Forbes ACM Transactions On Graphics (TOG) 38 (6), 1-11, 2019 | 284 | 2019 |
| A recurrent variational autoencoder for human motion synthesis T Komura, I Habibie, D Holden, J Schwarz, J Yearsley The 28th British Machine Vision Conference, 2017 | 240 | 2017 |
| Learned motion matching D Holden, O Kanoun, M Perepichka, T Popa ACM Transactions on Graphics (ToG) 39 (4), 53: 1-53: 12, 2020 | 202 | 2020 |
| Robust solving of optical motion capture data by denoising D Holden ACM Transactions on Graphics (TOG) 37 (4), 1-12, 2018 | 139 | 2018 |
| ZeroEGGS: Zero‐shot Example‐based Gesture Generation from Speech S Ghorbani, Y Ferstl, D Holden, NF Troje, MA Carbonneau Computer Graphics Forum 42 (1), 206-216, 2023 | 124 | 2023 |
| Subspace neural physics: Fast data-driven interactive simulation D Holden, BC Duong, S Datta, D Nowrouzezahrai Proceedings of the 18th annual ACM SIGGRAPH/Eurographics Symposium on …, 2019 | 120* | 2019 |
| Fast neural style transfer for motion data D Holden, I Habibie, I Kusajima, T Komura IEEE computer graphics and applications 37 (4), 42-49, 2017 | 114 | 2017 |
| Supertrack: Motion tracking for physically simulated characters using supervised learning L Fussell, K Bergamin, D Holden ACM Transactions on Graphics (TOG) 40 (6), 1-13, 2021 | 90 | 2021 |
| Learning an inverse rig mapping for character animation D Holden, J Saito, T Komura Proceedings of the 14th ACM SIGGRAPH/Eurographics Symposium on Computer …, 2015 | 45 | 2015 |
| Neural network ambient occlusion D Holden, J Saito, T Komura SIGGRAPH ASIA 2016 Technical Briefs, 1-4, 2016 | 25 | 2016 |
| Learning inverse rig mappings by nonlinear regression D Holden, J Saito, T Komura IEEE transactions on visualization and computer graphics 23 (3), 1167-1178, 2016 | 25 | 2016 |
| Robust marker trajectory repair for mocap using kinematic reference M Perepichka, D Holden, SP Mudur, T Popa Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and …, 2019 | 19 | 2019 |
| Humos: Human motion model conditioned on body shape S Tripathi, O Taheri, C Lassner, M Black, D Holden, C Stoll European Conference on Computer Vision, 133-152, 2024 | 18 | 2024 |
| Scanning and animating characters dressed in multiple-layer garments P Hu, T Komura, D Holden, Y Zhong The Visual Computer 33 (6), 961-969, 2017 | 16 | 2017 |
| Character control with neural networks and machine learning D Holden Proc. of GDC 1 (2018), 2, 2018 | 14 | 2018 |
| Artist guided generation of video game production quality face textures C Murphy, S Mudur, D Holden, MA Carbonneau, D Ghafourzadeh, ... Computers & Graphics 98, 268-279, 2021 | 12 | 2021 |
| Learning 3D Global Human Motion Estimation from Unpaired, Disjoint Datasets. J Habekost, T Shiratori, Y Ye, T Komura, M Shi, K Aberman, A Aristidou, ... BMVC, 2020 | 7 | 2020 |
