{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T18:29:33Z","timestamp":1781375373759,"version":"3.54.1"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>A fundamental problem in computer animation is that of realizing purposeful and realistic human movement given a sufficiently-rich set of motion capture clips. We learn data-driven generative models of human movement using autoregressive conditional variational autoencoders, or Motion VAEs. The latent variables of the learned autoencoder define the action space for the movement and thereby govern its evolution over time. Planning or control algorithms can then use this action space to generate desired motions. In particular, we use deep reinforcement learning to learn controllers that achieve goal-directed movements. We demonstrate the effectiveness of the approach on multiple tasks. We further evaluate system-design choices and describe the current limitations of Motion VAEs.<\/jats:p>","DOI":"10.1145\/3386569.3392422","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:44:27Z","timestamp":1597232667000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":223,"title":["Character controllers using motion VAEs"],"prefix":"10.1145","volume":"39","author":[{"given":"Hung Yu","family":"Ling","sequence":"first","affiliation":[{"name":"University of British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Zinno","sequence":"additional","affiliation":[{"name":"Electronic Arts Vancouver, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"George","family":"Cheng","sequence":"additional","affiliation":[{"name":"Electronic Arts Vancouver, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michiel","family":"Van De Panne","sequence":"additional","affiliation":[{"name":"University of British Columbia, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359566.3360070"},{"key":"e_1_2_2_2_1","unstructured":"Samy Bengio Oriol Vinyals Navdeep Jaitly and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Advances in Neural Information Processing Systems. 1171--1179."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356536"},{"key":"e_1_2_2_4_1","volume-title":"Openai gym. arXiv preprint arXiv:1606.01540","author":"Brockman Greg","year":"2016","unstructured":"Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. 2016. Openai gym. arXiv preprint arXiv:1606.01540 (2016)."},{"key":"e_1_2_2_5_1","volume-title":"Proc. of GDC.","author":"Clavet Simon","year":"2016","unstructured":"Simon Clavet. 2016. Motion matching and the road to next-gen animation. In Proc. of GDC."},{"key":"e_1_2_2_6_1","volume-title":"Go-explore: a new approach for hard-exploration problems. arXiv preprint arXiv:1901.10995","author":"Ecoffet Adrien","year":"2019","unstructured":"Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O Stanley, and Jeff Clune. 2019. Go-explore: a new approach for hard-exploration problems. arXiv preprint arXiv:1901.10995 (2019)."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/2919332.2919834"},{"key":"e_1_2_2_8_1","volume-title":"Meta learning shared hierarchies. arXiv preprint arXiv:1710.09767","author":"Frans Kevin","year":"2017","unstructured":"Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, and John Schulman. 2017. Meta learning shared hierarchies. arXiv preprint arXiv:1710.09767 (2017)."},{"key":"e_1_2_2_9_1","volume-title":"Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850","author":"Graves Alex","year":"2013","unstructured":"Alex Graves. 2013. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850 (2013)."},{"key":"e_1_2_2_10_1","volume-title":"World models. arXiv preprint arXiv:1803.10122","author":"Ha David","year":"2018","unstructured":"David Ha and J\u00fcrgen Schmidhuber. 2018. World models. arXiv preprint arXiv:1803.10122 (2018)."},{"key":"e_1_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Ikhsanul Habibie Daniel Holden Jonathan Schwarz Joe Yearsley and Taku Komura. 2017. A Recurrent Variational Autoencoder for Human Motion Synthesis.. In BMVC.","DOI":"10.5244\/C.31.119"},{"key":"e_1_2_2_12_1","unstructured":"Nicolas Heess Srinivasan Sriram Jay Lemmon Josh Merel Greg Wayne Yuval Tassa Tom Erez Ziyu Wang SM Eslami Martin Riedmiller et al. 2017. Emergence of locomotion behaviours in rich environments. arXiv preprint arXiv:1707.02286 (2017)."},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073663"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925975"},{"key":"e_1_2_2_15_1","volume-title":"Friedl De Groote, and C Karen Liu.","author":"Jiang Yifeng","year":"2019","unstructured":"Yifeng Jiang, Tom Van Wouwe, Friedl De Groote, and C Karen Liu. 2019. Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation. arXiv preprint arXiv:1904.13041 (2019)."},{"key":"e_1_2_2_16_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_2_2_17_1","volume-title":"Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114","author":"Kingma Diederik P","year":"2013","unstructured":"Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)."},{"key":"e_1_2_2_18_1","unstructured":"Ilya Kostrikov. 2018. PyTorch Implementations of Reinforcement Learning Algorithms. https:\/\/github.com\/ikostrikov\/pytorch-a2c-ppo-acktr-gail."},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566605"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gmod.2005.03.004"},{"key":"e_1_2_2_21_1","unstructured":"Kyungho Lee Seyoung Lee and Jehee Lee. 2018. Interactive character animation by learning multi-objective control. In SIGGRAPH Asia 2018 Technical Papers. ACM 180."},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322972"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1882261.1866160"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185524"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/1632592.1632598"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.497"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366172"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356501"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201311"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3099564.3099579"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186562.1015754"},{"key":"e_1_2_2_33_1","volume-title":"Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347","author":"Schulman John","year":"2017","unstructured":"John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)."},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356505"},{"key":"e_1_2_2_35_1","volume-title":"ACM Transactions on Graphics (tog)","author":"Treuille Adrien","unstructured":"Adrien Treuille, Yongjoon Lee, and Zoran Popovi\u0107. 2007. Near-optimal character animation with continuous control. In ACM Transactions on Graphics (tog), Vol. 26. ACM, 7."},{"key":"e_1_2_2_36_1","volume-title":"Arjan Egges, Zs M Ruttkay, and Mark H Overmars.","author":"Welbergen Herwin Van","year":"2010","unstructured":"Herwin Van Welbergen, Ben JH Van Basten, Arjan Egges, Zs M Ruttkay, and Mark H Overmars. 2010. Real time animation of virtual humans: a trade-off between naturalness and control. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 2530--2554."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","unstructured":"Z. Wang J. Chai and S. Xia. 2019. Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control. IEEE Transactions on Visualization and Computer Graphics (2019) 1--1. 10.1109\/TVCG.2019.2938520","DOI":"10.1109\/TVCG.2019.2938520"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356499"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_17"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201397"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201366"},{"key":"e_1_2_2_42_1","volume-title":"ICLR","author":"Zhou Yi","year":"2018","unstructured":"Yi Zhou, Zimo Li, Shuangjiu Xiao, Chong He, Zeng Huang, and Hao Li. 2018. Auto-conditioned recurrent networks for extended complex human motion synthesis. In ICLR 2018."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3386569.3392422","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3386569.3392422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T05:34:44Z","timestamp":1750829684000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3386569.3392422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,12]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3386569.3392422"],"URL":"https:\/\/doi.org\/10.1145\/3386569.3392422","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,12]]},"assertion":[{"value":"2020-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}