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Motion diffusion models and Reinforcement Learning (RL) based control for physics-based simulations have complementary strengths for human motion generation.
Trajectory optimization for full-body movements with complex contacts
Mazen Al Borno, Martin De Lasa, and Aaron Hertzmann · 2012
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SMPL: A skinned multi-person linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Physics-based motion capture imitation with deep reinforcement learning
Nuttapong Chentanez, Matthias Müller, Miles Macklin, Viktor Makoviychuk, and Stefan Jeschke · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Deepmimic: Example-guided deep reinforcement learning of physics-based character skills
Xue Bin Peng, Pieter Abbeel, Sergey Levine, and Michiel van de Panne · 2018
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Amass: Archive of motion capture as surface shapes
Naureen Mahmood, N. Ghorbani, N. Troje, Gerard Pons-Moll, and Michael J. Black · 2019
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Mcp: Learning composable hierarchical control with multiplicative compositional policies
Xue Bin Peng, Michael Chang, Grace Zhang, Pieter Abbeel, and Sergey Levine · 2019
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Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter
V Sanh · 2019
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Unicon: Universal neural controller for physics-based character motion
Tingwu Wang, Yunrong Guo, Maria Shugrina, and Sanja Fidler · 2020
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A scalable approach to control diverse behaviors for physically simulated characters
Jungdam Won, Deepak Gopinath, and Jessica Hodgins · 2020
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Residual force control for agile human behavior imitation and extended motion synthesis
Ye Yuan and Kris Kitani · 2020
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Supertrack: motion tracking for physically simulated characters using supervised learning
Levi Fussell, Kevin Bergamin, and Daniel Holden · 2021
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Dynamics-regulated kinematic policy for egocentric pose estimation
Zhengyi Luo, Ryo Hachiuma, Ye Yuan, and Kris Kitani · 2021
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Isaac gym: High performance gpu-based physics simulation for robot learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, et al · 2021
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Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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TEACH: Temporal Action Compositions for 3D Humans
Nikos Athanasiou, Mathis Petrovich, Michael J. Black, and Gül Varol · 2022
Cited alongside, same era.
Generating diverse and natural 3d human motions from text
Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang, Wei Ji, Xingyu Li, and Li Cheng · 2022
Cited alongside, same era.
Imagen video: High definition video generation with diffusion models
Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey A. Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, and Tim Salimans · 2022
Cited alongside, same era.
Padl: Language-directed physics-based character control
Jordan Juravsky, Yunrong Guo, Sanja Fidler, and Xue Bin Peng · 2022
Cited alongside, same era.
Embodied scene-aware human pose estimation
Zhengyi Luo, Shun Iwase, Ye Yuan, and Kris Kitani · 2022
Cited alongside, same era.
Physdiff: Physics-guided human motion diffusion model
Ye Yuan, Jiaming Song, Umar Iqbal, Arash Vahdat, and Jan Kautz · 2023
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Motionbert: A unified perspective on learning human motion representations
Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu, Libin Liu, Wayne Wu, and Yizhou Wang · 2023
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Generating human motion in 3d scenes from text descriptions
Zhi Cen, Huaijin Pi, Sida Peng, Zehong Shen, Minghui Yang, Shuai Zhu, Hujun Bao, and Xiaowei Zhou · 2024
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Taming diffusion probabilistic models for character control
Rui Chen, Mingyi Shi, Shaoli Huang, Ping Tan, Taku Komura, and Xuelin Chen · 2024
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Flexible motion in-betweening with diffusion models
Setareh Cohan, Guy Tevet, Daniele Reda, Xue Bin Peng, and Michiel van de Panne · 2024
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Iterative motion editing with natural language
Purvi Goel, Kuan-Chieh Wang, C Karen Liu, and Kayvon Fatahalian · 2024
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Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler · 2022
Cited alongside, same era.
TEMOS: Generating diverse human motions from textual descriptions
Mathis Petrovich, Michael J. Black, and Gül Varol · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
Motionclip: Exposing human motion generation to clip space
Guy Tevet, Brian Gordon, Amir Hertz, Amit H Bermano, and Daniel Cohen-Or · 2022
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song · 2023
Cited alongside, same era.
Mofusion: A framework for denoising-diffusion-based motion synthesis
Rishabh Dabral, Muhammad Hamza Mughal, Vladislav Golyanik, and Christian Theobalt · 2023
Cited alongside, same era.
Synthesizing physical character-scene interactions
Mohamed Hassan, Yunrong Guo, Tingwu Wang, Michael Black, Sanja Fidler, and Xue Bin Peng · 2023
Cited alongside, same era.
Closest in time.
Superpadl: Scaling language-directed physics-based control with progressive supervised distillation
Jordan Juravsky, Yunrong Guo, Sanja Fidler, and Xue Bin Peng · 2024
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Mas: Multi-view ancestral sampling for 3d motion generation using 2d diffusion
Roy Kapon, Guy Tevet, Daniel Cohen-Or, and Amit H Bermano · 2024
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Optimizing diffusion noise can serve as universal motion priors
Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, and Siyu Tang · 2024
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Nifty: Neural object interaction fields for guided human motion synthesis
Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, and Leonidas Guibas · 2024
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Genzi: Zero-shot 3d human-scene interaction generation
Lei Li and Angela Dai · 2024
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Human motion diffusion as a generative prior
Yoni Shafir, Guy Tevet, Roy Kapon, and Amit Haim Bermano · 2024
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Interactive character control with auto-regressive motion diffusion models
Yi Shi, Jingbo Wang, Xuekun Jiang, Bingkun Lin, Bo Dai, and Xue Bin Peng · 2024
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Pdp: Physics-based character animation via diffusion policy
Takara E Truong, Michael Piseno, Zhaoming Xie, and C Karen Liu · 2024
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Move as you say interact as you can: Language-guided human motion generation with scene affordance
Zan Wang, Yixin Chen, Baoxiong Jia, Puhao Li, Jinlu Zhang, Jingze Zhang, Tengyu Liu, Yixin Zhu, Wei Liang, and Siyuan Huang · 2024
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Thor: Text to human-object interaction diffusion via relation intervention
Qianyang Wu, Ye Shi, Xiaoshui Huang, Jingyi Yu, Lan Xu, and Jingya Wang · 2024
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Unified human-scene interaction via prompted chain-of-contacts
Zeqi Xiao, Tai Wang, Jingbo Wang, Jinkun Cao, Wenwei Zhang, Bo Dai, Dahua Lin, and Jiangmiao Pang · 2024
Closest in time.
Moconvq: Unified physics-based motion control via scalable discrete representations
Heyuan Yao, Zhenhua Song, Yuyang Zhou, Tenglong Ao, Baoquan Chen, and Libin Liu · 2024
Closest in time.
Generating human interaction motions in scenes with text control
Hongwei Yi, Justus Thies, Michael J Black, Xue Bin Peng, and Davis Rempe · 2024
Closest in time.