{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T08:18:23Z","timestamp":1783066703259,"version":"3.54.6"},"reference-count":85,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100000038","name":"NSERC","doi-asserted-by":"crossref","award":["RGPIN-2015-04843"],"award-info":[{"award-number":["RGPIN-2015-04843"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000046","name":"National Research Council Canada","doi-asserted-by":"publisher","award":["AI4D-166"],"award-info":[{"award-number":["AI4D-166"]}],"id":[{"id":"10.13039\/501100000046","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":[[2026,7,3]]},"abstract":"<jats:p>Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual characters. Adversarial imitation learning has been a highly effective method for learning motion priors from reference motion data. However, adversarial priors, with few exceptions, need to be retrained for each new controller, thereby limiting their reusability and necessitating the retention of the reference motion data when applied to downstream tasks. In this work, we present Score-Matching Motion Priors (SMP), which leverages pre-trained motion diffusion models and score distillation sampling (SDS) to create reusable task-agnostic motion priors. SMPs can be pre-trained on a motion dataset, independent of any control policy or task. Once trained, SMPs can be kept frozen and reused as general-purpose reward functions to train new policies to produce naturalistic behaviors for downstream tasks. We show that a general motion prior trained on large-scale datasets can be repurposed into a variety of style-specific priors. Furthermore, SMP can compose different styles to synthesize new styles not present in the original dataset. Our method can create reusable and modular motion priors that produce high-quality motions comparable to state-of-the-art adversarial imitation learning methods. In our experiments, we demonstrate the effectiveness of SMP across a diverse suite of control tasks with physically simulated humanoid characters. Video available at youtu.be\/jBA2tWk6vzU.<\/jats:p>","DOI":"10.1145\/3811282","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:05:51Z","timestamp":1783062351000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SMP: Reusable Score-Matching Motion Priors for Physics-Based Character Control"],"prefix":"10.1145","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7132-3155","authenticated-orcid":false,"given":"Yuxuan","family":"Mu","sequence":"first","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4823-3048","authenticated-orcid":false,"given":"Ziyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3360-6706","authenticated-orcid":false,"given":"Yi","family":"Shi","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3281-524X","authenticated-orcid":false,"given":"Dun","family":"Yang","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2139-4592","authenticated-orcid":false,"given":"Minami","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Sony Interactive Entertainment, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-2038-5864","authenticated-orcid":false,"given":"Kotaro","family":"Imamura","sequence":"additional","affiliation":[{"name":"Sony Interactive Entertainment, Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4376-2403","authenticated-orcid":false,"given":"Guy","family":"Tevet","sequence":"additional","affiliation":[{"name":"Stanford University, Stanford, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4539-0634","authenticated-orcid":false,"given":"Chuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Snap, Seattle, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4098-8482","authenticated-orcid":false,"given":"Michael","family":"Taylor","sequence":"additional","affiliation":[{"name":"Sony Interactive Entertainment, San Maeto, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6331-0522","authenticated-orcid":false,"given":"Chang","family":"Shu","sequence":"additional","affiliation":[{"name":"National Research Council Canada, Ottawa, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3236-5234","authenticated-orcid":false,"given":"Pengcheng","family":"Xi","sequence":"additional","affiliation":[{"name":"National Research Council Canada, Ottawa, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3677-5655","authenticated-orcid":false,"given":"Xue Bin","family":"Peng","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"},{"name":"NVIDIA, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356536"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2023.XIX.026"},{"key":"e_1_2_2_3_1","volume-title":"Generalized Biped Walking Control. ACM Transctions on Graphics 29, 4","author":"Coros Stelian","year":"2010","unstructured":"Stelian Coros, Philippe Beaudoin, and Michiel van de Panne. 2010. Generalized Biped Walking Control. ACM Transctions on Graphics 29, 4 (2010), Article 130."},{"key":"e_1_2_2_4_1","volume-title":"Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34 (2021), 8780\u20138794."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981973"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383287"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.1998.10487493"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01842"},{"key":"e_1_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413635"},{"key":"e_1_2_2_10_1","unstructured":"Yuan-Chen Guo Ying-Tian Liu Ruizhi Shao Christian Laforte Vikram Voleti Guan Luo Chia-Hao Chen Zi-Xin Zou Chen Wang Yan-Pei Cao and Song-Hai Zhang. 2023. threestudio: A unified framework for 3D content generation. https:\/\/github.com\/threestudio-project\/threestudio."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392480"},{"key":"e_1_2_2_12_1","volume-title":"Learning getting-up policies for real-world humanoid robots. arXiv preprint arXiv:2502.12152","author":"He Xialin","year":"2025","unstructured":"Xialin He, Runpei Dong, Zixuan Chen, and Saurabh Gupta. 2025. Learning getting-up policies for real-world humanoid robots. arXiv preprint arXiv:2502.12152 (2025)."},{"key":"e_1_2_2_13_1","volume-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_2_2_14_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_15_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems 33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840\u20136851."},{"key":"e_1_2_2_16_1","volume-title":"Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218414"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073663"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925975"},{"key":"e_1_2_2_20_1","volume-title":"Zhang (Eds.)","volume":"37","author":"Huang Bo-Ruei","year":"2024","unstructured":"Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, and Shao-Hua Sun. 2024b. Diffusion Imitation from Observation. In Advances in Neural Information Processing Systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (Eds.), Vol. 37. Curran Associates, Inc., 137190\u2013137217. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/f7faa46b563c2e5343a728c85bace833-Paper-Conference.pdf"},{"key":"e_1_2_2_21_1","volume-title":"Sophia Shao, Borivoje Nikolic, and Koushil Sreenath.","author":"Huang Xiaoyu","year":"2024","unstructured":"Xiaoyu Huang, Yufeng Chi, Ruofeng Wang, Zhongyu Li, Xue Bin Peng, Sophia Shao, Borivoje Nikolic, and Koushil Sreenath. 2024a. DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets. ArXiv abs\/2404.19264 (2024). https:\/\/api.semanticscholar.org\/CorpusID:269457018"},{"key":"e_1_2_2_22_1","volume-title":"Jessica Hodgins, Koushil Sreenath, and Farbod Farshidian.","author":"Huang Xiaoyu","year":"2025","unstructured":"Xiaoyu Huang, Takara Truong, Yunbo Zhang, Fangzhou Yu, Jean Pierre Sleiman, Jessica Hodgins, Koushil Sreenath, and Farbod Farshidian. 2025. Diffuse-CLoC: Guided Diffusion for Physics-based Character Look-ahead Control. arXiv preprint arXiv:2503.11801 (2025)."},{"key":"e_1_2_2_23_1","volume-title":"Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991","author":"Janner Michael","year":"2022","unstructured":"Michael Janner, Yilun Du, Joshua B Tenenbaum, and Sergey Levine. 2022. Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991 (2022)."},{"key":"e_1_2_2_24_1","volume-title":"Zhang (Eds.)","volume":"37","author":"Jiang Yanqin","year":"2024","unstructured":"Yanqin Jiang, Chaohui Yu, Chenjie Cao, Fan Wang, Weiming Hu, and Jin Gao. 2024. Animate3D: Animating Any 3D Model with Multi-view Video Diffusion. In Advances in Neural Information Processing Systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (Eds.), Vol. 37. Curran Associates, Inc., 125879\u2013125906. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/e3b53f89136b1bc69a5714ea465f01b6-Paper-Conference.pdf"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02282"},{"key":"e_1_2_2_26_1","volume-title":"Zhang (Eds.)","volume":"37","author":"Lai Chun-Mao","year":"2024","unstructured":"Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh, Yu-Chiang Frank Wang, Min-Hung Chen, and Shao-Hua Sun. 2024. Diffusion-Reward Adversarial Imitation Learning. In Advances in Neural Information Processing Systems, A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (Eds.), Vol. 37. Curran Associates, Inc., 95456\u201395487. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/ad47b1801557e4be37d30baf623de426-Paper-Conference.pdf"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1781155"},{"key":"e_1_2_2_28_1","first-page":"1","article-title":"Ganimator: Neural motion synthesis from a single sequence","volume":"41","author":"Li Peizhuo","year":"2022","unstructured":"Peizhuo Li, Kfir Aberman, Zihan Zhang, Rana Hanocka, and Olga Sorkine-Hornung. 2022. Ganimator: Neural motion synthesis from a single sequence. ACM Transactions on Graphics (TOG) 41, 4 (2022), 1\u201312.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00623"},{"key":"e_1_2_2_30_1","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 300\u2013309","author":"Lin Chen-Hsuan","year":"2023","unstructured":"Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2023. Magic3d: Highresolution text-to-3d content creation. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 300\u2013309."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392422"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2990496"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366173"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/1833349.1778865"},{"key":"e_1_2_2_35_1","volume-title":"Text-Aware Diffusion for Policy Learning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=nK6OnCpd3n","author":"Luo Calvin","year":"2024","unstructured":"Calvin Luo, Mandy He, Zilai Zeng, and Chen Sun. 2024. Text-Aware Diffusion for Policy Learning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=nK6OnCpd3n"},{"key":"e_1_2_2_36_1","unstructured":"Viktor Makoviychuk Lukasz Wawrzyniak Yunrong Guo Michelle Lu Kier Storey Miles Macklin David Hoeller Nikita Rudin Arthur Allshire Ankur Handa et al. 2021. Isaac gym: High performance gpu-based physics simulation for robot learning. arXiv preprint arXiv:2108.10470 (2021)."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3522618"},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1576246.1531387"},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.00506"},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2503.01143"},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356501"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275014"},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8460528"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459670"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.15607\/RSS.2018.XIV.008"},{"key":"e_1_2_2_47_1","volume-title":"Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988","author":"Poole Ben","year":"2022","unstructured":"Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. 2022. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988 (2022)."},{"key":"e_1_2_2_48_1","volume-title":"Single Motion Diffusion. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=DrhZneqz4n","author":"Raab Sigal","year":"2024","unstructured":"Sigal Raab, Inbal Leibovitch, Guy Tevet, Moab Arar, Amit Haim Bermano, and Daniel Cohen-Or. 2024. Single Motion Diffusion. In The Twelfth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=DrhZneqz4n"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/122718.122755"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01129"},{"key":"e_1_2_2_51_1","volume-title":"InsActor: Instruction-driven Physics-based Characters. NeurIPS","author":"Ren Jiawei","year":"2023","unstructured":"Jiawei Ren, Mingyuan Zhang, Cunjun Yu, Xiao Ma, Liang Pan, and Ziwei Liu. 2023. InsActor: Instruction-driven Physics-based Characters. NeurIPS (2023)."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASSP.1978.1163055"},{"key":"e_1_2_2_53_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_54_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_55_1","doi-asserted-by":"crossref","unstructured":"Agon Serifi Ruben Grandia Espen Knoop Markus Gross and Moritz B\u00e4cher. 2024. Robot motion diffusion model: Motion generation for robotic characters. In SIGGRAPH asia 2024 conference papers. 1\u20139.","DOI":"10.1145\/3680528.3687626"},{"key":"e_1_2_2_56_1","volume-title":"Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, 2387\u20132392","author":"Sharon Dana","unstructured":"Dana Sharon and Michiel van de Panne. 2005. Synthesis of controllers for stylized planar bipedal walking. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation. IEEE, 2387\u20132392."},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658140"},{"key":"e_1_2_2_58_1","volume-title":"Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)."},{"key":"e_1_2_2_59_1","volume-title":"Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_2_2_60_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=PxTIG12RRHS","author":"Song Yang","year":"2021","unstructured":"Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. 2021. Score-Based Generative Modeling through Stochastic Differential Equations. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=PxTIG12RRHS"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658209"},{"key":"e_1_2_2_62_1","unstructured":"Richard S Sutton Andrew G Barto et al. 1998. Reinforcement learning: An introduction. Vol. 1. MIT press Cambridge."},{"key":"e_1_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530697"},{"key":"e_1_2_2_64_1","volume-title":"Amit H Bermano, and Michiel van de Panne.","author":"Tevet Guy","year":"2024","unstructured":"Guy Tevet, Sigal Raab, Setareh Cohan, Daniele Reda, Zhengyi Luo, Xue Bin Peng, Amit H Bermano, and Michiel van de Panne. 2024. Closd: Closing the loop between simulation and diffusion for multi-task character control. arXiv preprint arXiv:2410.03441 (2024)."},{"key":"e_1_2_2_65_1","volume-title":"Human Motion Diffusion Model. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJ1kSyO2jwu","author":"Tevet Guy","year":"2023","unstructured":"Guy Tevet, Sigal Raab, Brian Gordon, Yoni Shafir, Daniel Cohen-or, and Amit Haim Bermano. 2023. Human Motion Diffusion Model. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJ1kSyO2jwu"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3680528.3687683"},{"key":"e_1_2_2_67_1","volume-title":"A connection between score matching and denoising autoencoders. Neural computation 23, 7","author":"Vincent Pascal","year":"2011","unstructured":"Pascal Vincent. 2011. A connection between score matching and denoising autoencoders. Neural computation 23, 7 (2011), 1661\u20131674."},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601192"},{"key":"e_1_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29470"},{"key":"e_1_2_2_70_1","doi-asserted-by":"crossref","unstructured":"Jack M Wang David J Fleet and Aaron Hertzmann. 2009. Optimizing walking controllers. In ACM SIGGRAPH Asia 2009 papers. 1\u20138.","DOI":"10.1145\/1661412.1618514"},{"key":"e_1_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0368"},{"key":"e_1_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1145\/378456.378507"},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130833"},{"key":"e_1_2_2_74_1","volume-title":"Diffusing States and Matching Scores: A New Framework for Imitation Learning. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=kWRKNDU6uN","author":"Wu Runzhe","year":"2025","unstructured":"Runzhe Wu, Yiding Chen, Gokul Swamy, Kiant\u00e9 Brantley, and Wen Sun. 2025a. Diffusing States and Matching Scores: A New Framework for Imitation Learning. In The Thirteenth International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=kWRKNDU6uN"},{"key":"e_1_2_2_75_1","volume-title":"UniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-Based Character Control. arXiv preprint arXiv:2504.12540","author":"Wu Yan","year":"2025","unstructured":"Yan Wu, Korrawe Karunratanakul, Zhengyi Luo, and Siyu Tang. 2025b. UniPhys: Unified Planner and Controller with Diffusion for Flexible Physics-Based Character Control. arXiv preprint arXiv:2504.12540 (2025)."},{"key":"e_1_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.1145\/3721238.3730616"},{"key":"e_1_2_2_77_1","unstructured":"Xinyu Xu Yizheng Zhang Yong-Lu Li Lei Han and Cewu Lu. 2024. HumanVLA: Towards Vision-Language Directed Object Rearrangement by Physical Humanoid. arXiv:2406.19972 [cs.RO] https:\/\/arxiv.org\/abs\/2406.19972"},{"key":"e_1_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555434"},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3658137"},{"key":"e_1_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276509"},{"key":"e_1_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00632"},{"key":"e_1_2_2_82_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275090"},{"key":"e_1_2_2_83_1","volume-title":"Motiondiffuse: Text-driven human motion generation with diffusion model. arXiv preprint arXiv:2208.15001","author":"Zhang Mingyuan","year":"2022","unstructured":"Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu. 2022. Motiondiffuse: Text-driven human motion generation with diffusion model. arXiv preprint arXiv:2208.15001 (2022)."},{"key":"e_1_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00589"},{"key":"e_1_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618397"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:40:52Z","timestamp":1783064452000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3811282"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,3]]},"references-count":85,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,7,3]]}},"alternative-id":["10.1145\/3811282"],"URL":"https:\/\/doi.org\/10.1145\/3811282","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,3]]},"assertion":[{"value":"2026-01-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-27","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}