{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T14:56:25Z","timestamp":1784904985532,"version":"3.55.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00222776"],"award-info":[{"award-number":["RS-2023-00222776"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006465","name":"Korea Creative Content Agency","doi-asserted-by":"publisher","award":["RS-2024-00399136"],"award-info":[{"award-number":["RS-2024-00399136"]}],"id":[{"id":"10.13039\/501100006465","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2025,8,1]]},"abstract":"<jats:p>We propose PhysicsFC, a method for controlling physically simulated football player characters to perform a variety of football skills-such as dribbling, trapping, moving, and kicking-based on user input, while seamlessly transitioning between these skills. Our skill-specific policies, which generate latent variables for each football skill, are trained using an existing physics-based motion embedding model that serves as a foundation for reproducing football motions. Key features include a tailored reward design for the Dribble policy, a two-phase reward structure combined with projectile dynamics-based initialization for the Trap policy, and a Data-Embedded Goal-Conditioned Latent Guidance (DEGCL) method for the Move policy. Using the trained skill policies, the proposed football player finite state machine (PhysicsFC FSM) allows users to interactively control the character. To ensure smooth and agile transitions between skill policies, as defined in the FSM, we introduce the Skill Transition-Based Initialization (STI), which is applied during the training of each skill policy. We develop several interactive scenarios to showcase PhysicsFC's effectiveness, including competitive trapping and dribbling, give-and-go plays, and 11v11 football games, where multiple PhysicsFC agents produce natural and controllable physics-based football player behaviors. Quantitative evaluations further validate the performance of individual skill policies and the transitions between them, using the presented metrics and experimental designs.<\/jats:p>","DOI":"10.1145\/3731425","type":"journal-article","created":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T04:02:22Z","timestamp":1753588942000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["PhysicsFC: Learning User-Controlled Skills for a Physics-Based Football Player Controller"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-3798-5290","authenticated-orcid":false,"given":"Minsu","family":"Kim","sequence":"first","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6652-5189","authenticated-orcid":false,"given":"Eunho","family":"Jung","sequence":"additional","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-5987","authenticated-orcid":false,"given":"Yoonsang","family":"Lee","sequence":"additional","affiliation":[{"name":"Hanyang University, Seoul, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591487"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356536"},{"key":"e_1_2_2_3_1","volume-title":"Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation. In 7th Annual Conference on Robot Learning.","author":"Chen Yuanpei","year":"2023","unstructured":"Yuanpei Chen, Chen Wang, Li Fei-Fei, and Karen Liu. 2023. Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation. In 7th Annual Conference on Robot Learning."},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1781156"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618205"},{"key":"e_1_2_2_6_1","volume-title":"MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters. In SIGGRAPH Asia 2023 Conference Papers (SA '23)","author":"Feng Yusen","year":"2023","unstructured":"Yusen Feng, Xiyan Xu, and Libin Liu. 2023. MuscleVAE: Model-Based Controllers of Muscle-Actuated Characters. In SIGGRAPH Asia 2023 Conference Papers (SA '23). Association for Computing Machinery, New York, NY, USA, 1\u201311."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adi8022"},{"key":"e_1_2_2_8_1","volume-title":"Proceedings of the 22nd annual conference on Computer graphics and interactive techniques (SIGGRAPH '95)","author":"Hodgins Jessica K.","unstructured":"Jessica K. Hodgins, Wayne L. Wooten, David C. Brogan, and James F. O'Brien. 1995. Animating human athletics. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques (SIGGRAPH '95). Association for Computing Machinery, New York, NY, USA, 71\u201378."},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322963"},{"key":"e_1_2_2_10_1","volume-title":"Advances in Neural Information Processing Systems","volume":"22","author":"Konidaris George","year":"2009","unstructured":"George Konidaris and Andrew Barto. 2009. Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining. In Advances in Neural Information Processing Systems, Vol. 22."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618187"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392432"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555489"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459774"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1778765.1781155"},{"key":"e_1_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661233"},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201315"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366173"},{"key":"e_1_2_2_19_1","volume-title":"From motor control to team play in simulated humanoid football. Science Robotics 7, 69","author":"Liu Siqi","year":"2022","unstructured":"Siqi Liu, Guy Lever, Zhe Wang, Josh Merel, S. M. Ali Eslami, Daniel Hennes, Wojciech M. Czarnecki, Yuval Tassa, Shayegan Omidshafiei, Abbas Abdolmaleki, Noah Y. Siegel, Leonard Hasenclever, Luke Marris, Saran Tunyasuvunakool, H. Francis Song, Markus Wulfmeier, Paul Muller, Tuomas Haarnoja, Brendan Tracey, Karl Tuyls, Thore Graepel, and Nicolas Heess. 2022. From motor control to team play in simulated humanoid football. Science Robotics 7, 69 (2022), eabo0235."},{"key":"e_1_2_2_20_1","volume-title":"Neural Probabilistic Motor Primitives for Humanoid Control. In 7th International Conference on Learning Representations, ICLR 2019","author":"Merel Josh","year":"2019","unstructured":"Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, and Nicolas Heess. 2019. Neural Probabilistic Motor Primitives for Humanoid Control. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6\u20139, 2019."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392474"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356501"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201311"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530110"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459670"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2626346"},{"key":"e_1_2_2_28_1","volume-title":"CALM: Conditional Adversarial Latent Models for Directable Virtual Characters. In ACM SIGGRAPH 2023 Conference Proceedings (SIGGRAPH '23)","author":"Tessler Chen","year":"2023","unstructured":"Chen Tessler, Yoni Kasten, Yunrong Guo, Shie Mannor, Gal Chechik, and Xue Bin Peng. 2023. CALM: Conditional Adversarial Latent Models for Directable Virtual Characters. In ACM SIGGRAPH 2023 Conference Proceedings (SIGGRAPH '23)."},{"key":"e_1_2_2_29_1","volume-title":"Strategy and Skill Learning for Physics-based Table Tennis Animation. In ACM SIGGRAPH 2024 Conference Papers (SIGGRAPH '24)","author":"Wang Jiashun","year":"2024","unstructured":"Jiashun Wang, Jessica Hodgins, and Jungdam Won. 2024. Strategy and Skill Learning for Physics-based Table Tennis Animation. In ACM SIGGRAPH 2024 Conference Papers (SIGGRAPH '24). New York, NY, USA, 1\u201311."},{"key":"e_1_2_2_30_1","first-page":"1","article-title":"Optimizing walking controllers. In ACM SIGGRAPH Asia 2009 papers (SIGGRAPH Asia '09). ACM, New York","volume":"168","author":"Wang Jack M.","year":"2009","unstructured":"Jack M. Wang, David J. Fleet, and Aaron Hertzmann. 2009. Optimizing walking controllers. In ACM SIGGRAPH Asia 2009 papers (SIGGRAPH Asia '09). ACM, New York, NY, USA, 168:1\u2013168:8.","journal-title":"NY, USA"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459761"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530067"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14115"},{"key":"e_1_2_2_34_1","volume-title":"Learning Soccer Juggling Skills with Layer-Wise Mixture-of-Experts. In ACM SIGGRAPH 2022 Conference Proceedings (SIGGRAPH '22)","author":"Xie Zhaoming","unstructured":"Zhaoming Xie, Sebastian Starke, Hung Yu Ling, and Michiel van de Panne. 2022. Learning Soccer Juggling Skills with Layer-Wise Mixture-of-Experts. In ACM SIGGRAPH 2022 Conference Proceedings (SIGGRAPH '22). Article 25, 9 pages."},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592447"},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555434"},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658137"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276509"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459817"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592408"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618397"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3731425","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T17:57:23Z","timestamp":1774634243000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3731425"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,27]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,8,1]]}},"alternative-id":["10.1145\/3731425"],"URL":"https:\/\/doi.org\/10.1145\/3731425","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,27]]},"assertion":[{"value":"2025-07-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}