“DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds” by Starke, Mason and Komura

  • ©Sebastian Starke, Ian Mason, and Taku Komura



    DeepPhase: Periodic Autoencoders for Learning Motion Phase Manifolds

Program Title:

    Demo Labs



    Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis. In this work, we propose a novel neural network architecture called the Periodic Autoencoder that can learn periodic features from large unstructured motion datasets in an unsupervised manner. The character movements are decomposed into multiple latent channels that capture the non-linear periodicity of different body segments while progressing forward in time. Our method extracts a multi-dimensional phase space from full-body motion data, which effectively clusters animations and produces a manifold in which computed feature distances provide a better similarity measure than in the original motion space to achieve better temporal and spatial alignment. We demonstrate that the learned periodic embedding can significantly help to improve neural motion synthesis in a number of tasks, including diverse locomotion skills, style-based movements, dance motion synthesis from music, synthesis of dribbling motions in football, and motion query for matching poses within large animation databases.


    1. Okan Arikan and David A Forsyth. 2002. Interactive motion generation from examples. ACM Trans on Graph 21, 3 (2002), 483–490. 
    2. Philippe Beaudoin, Pierre Poulin, and Michiel van de Panne. 2007. Adapting wavelet compression to human motion capture clips. In Proceedings of Graphics Interface 2007. 313–318.
    3. Kevin Bergamin, Simon Clavet, Daniel Holden, and James Richard Forbes. 2019. DReCon: data-driven responsive control of physics-based characters. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1–11.
    4. Armin Bruderlin and Lance Williams. 1995. Motion signal processing. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques. 97–104.
    5. Kyungmin Cho, Chaelin Kim, Jungjin Park, Joonkyu Park, and Junyong Noh. 2021. Motion recommendation for online character control. ACM Transactions on Graphics (TOG) 40, 6 (2021), 1–16.
    6. Simon Clavet. 2016. Motion matching and the road to next-gen animation. In Proc. of GDC.
    7. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 4171–4186. 
    8. Milan R Dimitrijevic, Yuri Gerasimenko, and Michaela M Pinter. 1998. Evidence for a spinal central pattern generator in humans. Annals of the New York Academy of Sciences 860, 1 (1998), 360–376.
    9. Levi Fussell, Kevin Bergamin, and Daniel Holden. 2021. SuperTrack: motion tracking for physically simulated characters using supervised learning. ACM Transactions on Graphics (TOG) 40, 6 (2021), 1–13.
    10. Félix G Harvey, Mike Yurick, Derek Nowrouzezahrai, and Christopher Pal. 2020. Robust motion in-betweening. ACM Transactions on Graphics (TOG) 39, 4 (2020), 60–1.
    11. Rachel Heck and Michael Gleicher. 2007. Parametric motion graphs. In Proc. I3D. 129–136. 
    12. Gustav Eje Henter, Simon Alexanderson, and Jonas Beskow. 2020. Moglow: Probabilistic and controllable motion synthesis using normalising flows. ACM Transactions on Graphics (TOG) 39, 6 (2020), 1–14.
    13. Jonathan Ho and Stefano Ermon. 2016. Generative adversarial imitation learning. Advances in neural information processing systems 29 (2016), 4565–4573.
    14. Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned neural networks for character control. ACM Trans on Graph 36, 4 (2017), 42. 
    15. Daniel Holden, Jun Saito, and Taku Komura. 2016. A deep learning framework for character motion synthesis and editing. ACM Trans on Graph 35, 4 (2016), 138.
    16. Daniel Holden, Jun Saito, Taku Komura, and Thomas Joyce. 2015. Learning motion manifolds with convolutional autoencoders. In SIGGRAPH Asia 2015 Technical Briefs. ACM, 18.
    17. Lucas Kovar and Michael Gleicher. 2004. Automated Extraction and Parameterization of Motions in Large Data Sets. ACM Trans on Graph 23, 3 (2004), 559–568. 
    18. Lucas Kovar, Michael Gleicher, and Frédéric Pighin. 2008. Motion graphs. In ACM SIGGRAPH 2008 classes. 1–10.
    19. Jehee Lee, Jinxiang Chai, Paul SA Reitsma, Jessica K Hodgins, and Nancy S Pollard. 2002. Interactive control of avatars animated with human motion data. ACM Trans on Graph 21, 3 (2002), 491–500. 
    20. Kyungho Lee, Seyoung Lee, and Jehee Lee. 2018. Interactive character animation by learning multi-objective control. In SIGGRAPH Asia 2018 Technical Papers. ACM, 180.
    21. Kyungho Lee, Sehee Min, Sunmin Lee, and Jehee Lee. 2021b. Learning Time-Critical Responses for Interactive Character Control. ACM Trans. Graph. 40, 4, Article 147 (2021).
    22. Seyoung Lee, Sunmin Lee, Yongwoo Lee, and Jehee Lee. 2021a. Learning a family of motor skills from a single motion clip. ACM Trans. Graph. 40, 4, Article 93 (2021).
    23. Yongjoon Lee, Kevin Wampler, Gilbert Bernstein, Jovan Popović, and Zoran Popović. 2010. Motion fields for interactive character locomotion. In ACM SIGGRAPH Asia 2010 papers. 1–8.
    24. Sergey Levine, Jack M Wang, Alexis Haraux, Zoran Popović, and Vladlen Koltun. 2012. Continuous character control with low-dimensional embeddings. ACM Transactions on Graphics (TOG) 31, 4 (2012), 1–10.
    25. Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. 2021. Learn to Dance with AIST++: Music Conditioned 3D Dance Generation. arXiv:2101.08779 [cs.CV]
    26. Hung Yu Ling, Fabio Zinno, George Cheng, and Michiel Van De Panne. 2020. Character controllers using motion vaes. ACM Transactions on Graphics (TOG) 39, 4 (2020), 40–1.
    27. Zicheng Liu, Steven J Gortler, and Michael F Cohen. 1994. Hierarchical spacetime control. In Proceedings of the 21st annual conference on Computer graphics and interactive techniques. 35–42.
    28. Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In International Conference on Learning Representations.
    29. Ying-Sheng Luo, Jonathan Hans Soeseno, Trista Pei-Chun Chen, and Wei-Chao Chen. 2020. Carl: Controllable agent with reinforcement learning for quadruped locomotion. ACM Transactions on Graphics (TOG) 39, 4 (2020), 38–1.
    30. Michal Mach and Maksym Zhuravlov. 2021. Motion Matching in ‘The Last of Us Part II’. https://www.gdcvault.com/play/1027118/Motion-Matching-in-The-Last.
    31. Ian Mason, Sebastian Starke, and Taku Komura. 2022. Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases. arXiv preprint arXiv:2201.04439 (2022).
    32. Josh Merel, Saran Tunyasuvunakool, Arun Ahuja, Yuval Tassa, Leonard Hasenclever, Vu Pham, Tom Erez, Greg Wayne, and Nicolas Heess. 2020. Catch & Carry: reusable neural controllers for vision-guided whole-body tasks. ACM Transactions on Graphics (TOG) 39, 4 (2020), 39–1.
    33. Jianyuan Min and Jinxiang Chai. 2012. Motion graphs++: a compact generative model for semantic motion analysis and synthesis. ACM Transactions on Graphics (TOG) 31, 6 (2012), 153.
    34. Tomohiko Mukai and Shigeru Kuriyama. 2005. Geostatistical motion interpolation. ACM Trans on Graph 24, 3 (2005), 1062–1070. 
    35. Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu. 2017. Neural discrete representation learning. arXiv preprint arXiv:1711.00937 (2017).
    36. Soohwan Park, Hoseok Ryu, Seyoung Lee, Sunmin Lee, and Jehee Lee. 2019. Learning predict-and-simulate policies from unorganized human motion data. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1–11.
    37. Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne. 2018. Deepmimic: Example-guided deep reinforcement learning of physics-based character skills. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1–14.
    38. Xue Bin Peng, Glen Berseth, and Michiel van de Panne. 2016. Terrain-Adaptive Locomotion Skills Using Deep Reinforcement Learning. ACM Trans on Graph 35, 4 (2016). 
    39. Xue Bin Peng, Glen Berseth, KangKang Yin, and Michiel Van De Panne. 2017. Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning. ACM Transactions on Graphics (TOG) 36, 4 (2017), 1–13.
    40. Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa. 2021. AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control. arXiv preprint arXiv:2104.02180 (2021).
    41. Charles Rose, Michael F Cohen, and Bobby Bodenheimer. 1998. Verbs and adverbs: Multidimensional motion interpolation. IEEE Computer Graphics and Applications 18, 5 (1998), 32–40.
    42. Charles F Rose III, Peter-Pike J Sloan, and Michael F Cohen. 2001. Artist-Directed Inverse-Kinematics Using Radial Basis Function Interpolation. Computer Graphics Forum 20, 3 (2001), 239–250. 
    43. Alla Safonova and Jessica K Hodgins. 2007. Construction and optimal search of interpolated motion graphs. ACM Trans on Graph 26, 3 (2007). 
    44. Hyun Joon Shin and Hyun Seok Oh. 2006. Fat graphs: constructing an interactive character with continuous controls. In Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation. Eurographics Association, 291–298.
    45. Sebastian Starke, He Zhang, Taku Komura, and Jun Saito. 2019. Neural state machine for character-scene interactions. ACM Trans on Graph 38, 6 (2019), 209. 
    46. Sebastian Starke, Yiwei Zhao, Taku Komura, and Kazi Zaman. 2020. Local motion phases for learning multi-contact character movements. ACM Transactions on Graphics (TOG) 39, 4 (2020), 54–1.
    47. Munetoshi Unuma, Ken Anjyo, and Ryozo Takeuchi. 1995. Fourier principles for emotion-based human figure animation. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques. 91–96.
    48. Guillermo Valle-Pérez, Gustav Eje Henter, Jonas Beskow, André Holzapfel, Pierre-Yves Oudeyer, and Simon Alexanderson. 2021. Transflower: probabilistic autoregressive dance generation with multimodal attention. ACM Transactions on Graphics (TOG) 40, 6 (2021), 1–14.
    49. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998–6008.
    50. Douglas J Wiley and James K Hahn. 1997. Interpolation synthesis of articulated figure motion. IEEE Computer Graphics and Applications 17, 6 (1997), 39–45.
    51. Jungdam Won, Deepak Gopinath, and Jessica Hodgins. 2020. A scalable approach to control diverse behaviors for physically simulated characters. ACM Transactions on Graphics (TOG) 39, 4 (2020), 33–1.
    52. M Ersin Yumer and Niloy J Mitra. 2016. Spectral style transfer for human motion between independent actions. ACM Transactions on Graphics (TOG) 35, 4 (2016), 1–8.
    53. Rafael Yuste, Jason N MacLean, Jeffrey Smith, and Anders Lansner. 2005. The cortex as a central pattern generator. Nature Reviews Neuroscience 6, 6 (2005), 477–483.
    54. He Zhang, Sebastian Starke, Taku Komura, and Jun Saito. 2018. Mode-adaptive neural networks for quadruped motion control. ACM Trans on Graph 37, 4 (2018). 

ACM Digital Library Publication: