arXiv:2509. 15443v2 Announce Type: replace-cross Abstract: Human-to-humanoid imitation learning presents a promising pathway to address the severe data scarcity bottleneck in robotics by utilizing abundant, large-scale human motion collections.
By Xingyu Chen, Hanyu Wu, Sikai Wu, Mingliang Zhou, Diyun Xiang, Haodong Zhang, Yangchen Zhou, Yukang Gao, Yi Gu, Renjing Xu
arXiv:2511. 20532v3 Announce Type: replace-cross Abstract: The primary output of the nervous system is movement and behavior.
By Charles Y. Zhang (Harvard University), Yuanjia Yang (Salk Institute for Biological Studies), Aidan Sirbu (Mila), Elliott T. T. Abe (University of Washington), Emil W\"arnberg (Harvard University), Eric J. Leonardis (Salk Institute for Biological Studies), Diego E. Aldarondo (Harvard University), Adam Lee (Harvard University), Aaditya Prasad (Massachusetts Institute of Technology), Jason Foat (Salk Institute for Biological Studies), Kaiwen Bian (Salk Institute for Biological Studies), Joshua Park (Salk Institute for Biological Studies), Rusham Bhatt (Salk Institute for Biological Studies), Vyom N. Patel (Neuromatch), Hutton Saunders (Salk Institute for Biological Studies), Austin O. Barbano (Salk Institute for Biological Studies), Akira Nagamori (Salk Institute for Biological Studies), Ayesha R. Thanawalla (Salk Institute for Biological Studies), Kee Wui Huang (Salk Institute for Biological Studies), Fabian Plum (Imperial College London), Hendrik K. Beck (Imperial College London), Steven W. Flavell (Massachusetts Institute of Technology), David Labonte (Imperial College London), Blake A. Richards (Mila), Bingni W. Brunton (University of Washington), Eiman Azim (Salk Institute for Biological Studies), Bence P. \"Olveczky (Harvard University), Talmo D. Pereira (Salk Institute for Biological Studies)
arXiv:2608.23279v1 Announce Type: new
Abstract: Text-driven human motion synthesis has made substantial development with two core modules of motion representation and generative architecture. For rep...
By Chengqun Yang, Liang Xu, Yanping Li, Fulong Liu, Jingnan Gao, Weili Zeng, Yichao Yan
arXiv:2608. 04389v1 Announce Type: new Abstract: Decoding continuous motor trajectories from neural activity is essential for developing practical brain-computer interfaces (BCIs).
By Luyao Jin, Yonghao Song, Huan Zhao, Vincent C. K. Cheung, Wei-Hsin Liao
STyMo is a few‑shot motion style transfer method that learns from only seconds of paired data and trains in one to two minutes. It decomposes style into a static posture component and a temporal dynamics component, allowing runtime adjustment of posture intensity, temporal exaggeration, and per‑body‑region style. The approach includes a stylizability gate to avoid artifacts on out‑of‑distribution motions and supports an iterative authoring workflow, with results shown across a range of motion styles and a released dataset for future research.
By Jose Luis Ponton, Alexander Winkler, Ladislav Kavan, Yuting Ye, Petr Kadlecek
arXiv:2606. 09842v1 Announce Type: cross Abstract: Applying Human Pose Estimation (HPE) in real world environments remains a challenging task, this paper explores and surveys real time HPE approaches and their limitations in sports analysis for individuals, alongside developing a practical lightweight prototype for real world testing and usage.
By Parth Agrawal, Ronit, Sagar Kumar, Aashish Bhambri