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This paper focuses on a critical challenge in robotics: translating text-driven human motions into executable actions for humanoid robots, enabling efficient and cost-effective learning of new behaviors.
Carnegie-mellon mocap database
Carnegie Mellon University · 2007
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Movi: A large multi-purpose human motion and video dataset
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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
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Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano · 2022
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Mazeyu Ji, Xuanbin Peng, Fangchen Liu, Jialong Li, Ge Yang, Xuxin Cheng, and Xiaolong Wang · 2024
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Motiondiffuse: Text-driven human motion generation with diffusion model
Mingyuan Zhang, Zhongang Cai, Liang Pan, Fangzhou Hong, Xinying Guo, Lei Yang, and Ziwei Liu · 2024
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Learning generalizable human motion generator with reinforcement learning
Yunyao Mao, Xiaoyang Liu, Wengang Zhou, Zhenbo Lu, and Houqiang Li · 2024
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Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng · 2024
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Jonathan Tseng, Rodrigo Castellon, and Karen Liu · 2023
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Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, and Ying Shan · 2023
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Motiongpt-2: A general-purpose motion-language model for motion generation and understanding
Yuan Wang, Di Huang, Yaqi Zhang, Wanli Ouyang, Jile Jiao, Xuetao Feng, Yan Zhou, Pengfei Wan, Shixiang Tang, and Dan Xu
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Quo vadis, motion generation? from large language models to large motion models
Ye Wang, Sipeng Zheng, Bin Cao, Qianshan Wei, Qin Jin, and Zongqing Lu
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Learning human-to-humanoid real-time whole-body teleoperation
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Humanoid-gym: Reinforcement learning for humanoid robot with zero-shot sim2real transfer
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Reindiffuse: Crafting physically plausible motions with reinforced diffusion model
Gaoge Han, Mingjiang Liang, Jinglei Tang, Yongkang Cheng, Wei Liu, and Shaoli Huang · 2025
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