{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T03:16:39Z","timestamp":1774667799334,"version":"3.50.1"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2023,8,16]],"date-time":"2023-08-16T00:00:00Z","timestamp":1692144000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ANR","award":["ANR-18-EURE-0022"],"award-info":[{"award-number":["ANR-18-EURE-0022"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Comput. Graph. Interact. Tech."],"published-print":{"date-parts":[[2023,8,16]]},"abstract":"<jats:p>Simulating realistic interaction and motions for physics-based characters is of great interest for interactive applications, and automatic secondary character animation in the movie and video game industries. Recent works in reinforcement learning have proposed impressive results for single character simulation, especially the ones that use imitation learning based techniques. However, imitating multiple characters interactions and motions requires to also model their interactions. In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters. Two unstructured datasets are given as inputs: 1) a single-actor dataset containing motions of a single actor performing a set of motions linked to a specific application, and 2) an interaction dataset containing a few examples of interactions between multiple actors. Based on these datasets, our system trains control policies allowing each character to imitate the interactive skills associated with each actor, while preserving the intrinsic style. This approach has been tested on two different fighting styles, boxing and full-body martial art, to demonstrate the ability of the method to imitate different styles.<\/jats:p>","DOI":"10.1145\/3606926","type":"journal-article","created":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T10:05:30Z","timestamp":1692871530000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["MAAIP"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4831-3343","authenticated-orcid":false,"given":"Mohamed","family":"Younes","sequence":"first","affiliation":[{"name":"Inria, IRISA, University of Rennes, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0232-1097","authenticated-orcid":false,"given":"Ewa","family":"Kijak","sequence":"additional","affiliation":[{"name":"University of Rennes, Inria, IRISA, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1863-8921","authenticated-orcid":false,"given":"Richard","family":"Kulpa","sequence":"additional","affiliation":[{"name":"University Rennes 2, Inria, M2S, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9663-562X","authenticated-orcid":false,"given":"Simon","family":"Malinowski","sequence":"additional","affiliation":[{"name":"University of Rennes, Inria, IRISA, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2690-0077","authenticated-orcid":false,"given":"Franck","family":"Multon","sequence":"additional","affiliation":[{"name":"University of Rennes, Inria, IRISA, M2S, Rennes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2007.913919"},{"key":"e_1_2_2_2_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Christianos Filippos","year":"2021","unstructured":"Filippos Christianos, Georgios Papoudakis, Muhammad A Rahman, and Stefano V Albrecht. 2021. Scaling multi-agent reinforcement learning with selective parameter sharing. In International Conference on Machine Learning. PMLR, 1989--1998."},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1781156"},{"key":"e_1_2_2_4_1","volume-title":"Computer Graphics Forum","author":"Silva Marco Da","unstructured":"Marco Da Silva, Yeuhi Abe, and Jovan Popovi\u0107. 2008. Simulation of human motion data using short-horizon model-predictive control. In Computer Graphics Forum, Vol. 27. Wiley Online Library, 371--380."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.1998.10487493"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2767002"},{"key":"e_1_2_2_8_1","volume-title":"Erwan Le Merrer, and Bruno Sericola","author":"Hardy Corentin","year":"2019","unstructured":"Corentin Hardy, Erwan Le Merrer, and Bruno Sericola. 2019. Md-gan: Multi-discriminator generative adversarial networks for distributed datasets. In 2019 IEEE international parallel and distributed processing symposium (IPDPS). IEEE, 866--877."},{"key":"e_1_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Brandon Haworth Glen Berseth Seonghyeon Moon Petros Faloutsos and Mubbasir Kapadia. 2020. Deep integration of physical humanoid control and crowd navigation. In Motion Interaction and Games. 1--10.","DOI":"10.1145\/3424636.3426894"},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1778770"},{"key":"e_1_2_2_11_1","volume-title":"Generative adversarial imitation learning. Advances in neural information processing systems 29","author":"Ho Jonathan","year":"2016","unstructured":"Jonathan Ho and Stefano Ermon. 2016. Generative adversarial imitation learning. Advances in neural information processing systems 29 (2016)."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218414"},{"key":"e_1_2_2_13_1","volume-title":"PADL: Language-Directed Physics-Based Character Control. In SIGGRAPH Asia 2022 Conference Papers (SA '22 Conference Papers),.","author":"Juravsky Jordan","year":"2022","unstructured":"Jordan Juravsky, Yunrong Guo, Sanja Fidler, and Xue Bin Peng. 2022. PADL: Language-Directed Physics-Based Character Control. In SIGGRAPH Asia 2022 Conference Papers (SA '22 Conference Papers),."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2983616"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1781155"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-021-02269-1"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3091574.3091594"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1218064.1218093"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980179.2982424"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1778865"},{"key":"e_1_2_2_21_1","volume-title":"Daniel Hennes, Wojciech M Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, et al.","author":"Liu Siqi","year":"2022","unstructured":"Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, SM Ali Eslami, Daniel Hennes, Wojciech M Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, et al. 2022. From motor control to team play in simulated humanoid football. Science Robotics 7, 69 (2022), eabo0235."},{"key":"e_1_2_2_22_1","volume-title":"OpenAI Pieter Abbeel, and Igor Mordatch","author":"Lowe Ryan","year":"2017","unstructured":"Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017. Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_2_23_1","volume-title":"Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning.","author":"Makoviychuk Viktor","year":"2021","unstructured":"Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State. 2021. Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning."},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"e_1_2_2_25_1","volume-title":"Learning human behaviors from motion capture by adversarial imitation. arXiv preprint arXiv:1707.02201","author":"Merel Josh","year":"2017","unstructured":"Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, and Nicolas Heess. 2017. Learning human behaviors from motion capture by adversarial imitation. arXiv preprint arXiv:1707.02201 (2017)."},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1778808"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1966394.1966395"},{"key":"e_1_2_2_28_1","unstructured":"Vinod Nair and Geoffrey E Hinton. 2010. Rectified linear units improve restricted boltzmann machines. In Icml."},{"key":"e_1_2_2_29_1","volume-title":"Dual discriminator generative adversarial nets. Advances in neural information processing systems 30","author":"Nguyen Tu","year":"2017","unstructured":"Tu Nguyen, Trung Le, Hung Vu, and Dinh Phung. 2017. Dual discriminator generative adversarial nets. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201311"},{"key":"e_1_2_2_31_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3072959.3073602","article-title":"Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning","volume":"36","author":"Peng Xue Bin","year":"2017","unstructured":"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.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_2_32_1","volume-title":"ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters. arXiv preprint arXiv:2205.01906","author":"Peng Xue Bin","year":"2022","unstructured":"Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler. 2022. ASE: Large-Scale Reusable Adversarial Skill Embeddings for Physically Simulated Characters. arXiv preprint arXiv:2205.01906 (2022)."},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459670"},{"key":"e_1_2_2_34_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 661--668","author":"Ross St\u00e9phane","year":"2010","unstructured":"St\u00e9phane Ross and Drew Bagnell. 2010. Efficient reductions for imitation learning. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 661--668."},{"key":"e_1_2_2_35_1","volume-title":"High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438","author":"Schulman John","year":"2015","unstructured":"John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel. 2015. High-dimensional continuous control using generalized advantage estimation. arXiv preprint arXiv:1506.02438 (2015)."},{"key":"e_1_2_2_36_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_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2010.257"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409060.1409067"},{"key":"e_1_2_2_39_1","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Song Jiaming","year":"2018","unstructured":"Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon. 2018. Multi-Agent Generative Adversarial Imitation Learning. In Advances in Neural Information Processing Systems, Vol. 31. https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/240c945bb72980130446fc2b40fbb8e0-Paper.pdf"},{"key":"e_1_2_2_40_1","volume-title":"Learning to predict by the methods of temporal differences. Machine learning 3, 1","author":"Sutton Richard S","year":"1988","unstructured":"Richard S Sutton. 1988. Learning to predict by the methods of temporal differences. Machine learning 3, 1 (1988), 9--44."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCG.2011.30"},{"key":"e_1_2_2_42_1","volume-title":"Revisiting parameter sharing in multi-agent deep reinforcement learning. arXiv preprint arXiv:2005.13625","author":"Terry Justin K","year":"2020","unstructured":"Justin K Terry, Nathaniel Grammel, Ananth Hari, Luis Santos, and Benjamin Black. 2020. Revisiting parameter sharing in multi-agent deep reinforcement learning. arXiv preprint arXiv:2005.13625 (2020)."},{"key":"e_1_2_2_43_1","volume-title":"Generative adversarial imitation from observation. arXiv preprint arXiv:1807.06158","author":"Torabi Faraz","year":"2018","unstructured":"Faraz Torabi, Garrett Warnell, and Peter Stone. 2018. Generative adversarial imitation from observation. arXiv preprint arXiv:1807.06158 (2018)."},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2542067"},{"key":"e_1_2_2_45_1","volume-title":"Robust imitation of diverse behaviors. Advances in Neural Information Processing Systems 30","author":"Wang Ziyu","year":"2017","unstructured":"Ziyu Wang, Josh S Merel, Scott E Reed, Nando de Freitas, Gregory Wayne, and Nicolas Heess. 2017. Robust imitation of diverse behaviors. Advances in Neural Information Processing Systems 30 (2017)."},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459761"},{"key":"e_1_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356499"},{"key":"e_1_2_2_48_1","volume-title":"Nam Hee Kim, and Michiel van de Panne.","author":"Xie Zhaoming","year":"2020","unstructured":"Zhaoming Xie, Hung Yu Ling, Nam Hee Kim, and Michiel van de Panne. 2020. Allsteps: curriculum-driven learning of stepping stone skills. In Computer Graphics Forum, Vol. 39. Wiley Online Library, 213--224."},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3480148"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459817"},{"key":"e_1_2_2_51_1","volume-title":"The surprising effectiveness of ppo in cooperative, multi-agent games. arXiv preprint arXiv:2103.01955","author":"Yu Chao","year":"2021","unstructured":"Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu. 2021. The surprising effectiveness of ppo in cooperative, multi-agent games. arXiv preprint arXiv:2103.01955 (2021)."},{"key":"e_1_2_2_52_1","volume-title":"Aaai","volume":"8","author":"Ziebart Brian D","year":"2008","unstructured":"Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al. 2008. Maximum entropy inverse reinforcement learning.. In Aaai, Vol. 8. Chicago, IL, USA, 1433--1438."},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/545261.545276"}],"container-title":["Proceedings of the ACM on Computer Graphics and Interactive Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3606926","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3606926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:48:52Z","timestamp":1750182532000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3606926"}},"subtitle":["Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters"],"short-title":[],"issued":{"date-parts":[[2023,8,16]]},"references-count":53,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,8,16]]}},"alternative-id":["10.1145\/3606926"],"URL":"https:\/\/doi.org\/10.1145\/3606926","relation":{},"ISSN":["2577-6193"],"issn-type":[{"value":"2577-6193","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,16]]},"assertion":[{"value":"2023-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}