arXiv:2609.01276v1 Announce Type: new
Abstract: Complete 3D perception from egocentric video requires recovering the surrounding scene and the wearer's full-body motion in a shared metric frame. Exis...
By Kai Guan, Minchao Jiang, Ruichen WangLi, Wentao Zhu, Lei Zhang
arXiv:2607. 13646v1 Announce Type: cross Abstract: Recent advances in 3D human reconstruction have improved overall performance, yet current models still fail in the most challenging real-world scenarios.
By Tianshun Han, Ziyu Shi, Lijian Liu, Ajian Liu, Benjia Zhou, Hugo Jair Escalante, Yanyan Liang, Sergio Escalera, Zhen Lei, Jun Wan
arXiv:2608.29927v1 Announce Type: new
Abstract: We address the problem of 3D body pose estimation of multiple interacting people from their egocentric views with centralized coordination. Each indivi...
By Daeyun Shin, Yunhan Zhao, Shu Kong, Alexander C. Berg, Charless Fowlkes
arXiv:2606. 28215v1 Announce Type: cross Abstract: Extracting dynamic 4D object interactions from massive, in-the-wild monocular videos offers a highly efficient data collection pathway for scaling Embodied AI and training VLAs.
By Jiaxin Li, Yuxiang Wu, Zhenkai Zhang, Xinrui Shi, Haoyuan Wang, Yichen Zhao, Su Linxiang, Chenyang Yu, Mingyu Zhang, Yifan Ding, Boran Wen, Li Zhang, Ruiyang Liu, Yong-Lu Li
arXiv:2609.08636v1 Announce Type: cross
Abstract: Egocentric 4D interaction forecasting aims to anticipate both where future interactions will occur in 3D and how the human body will move to realize...
By Qiaohui Chu, Haoyu Zhang, Meng Liu, Haoxiang Shi, Dongmei Jiang, Liqiang Nie
arXiv:2607. 15868v1 Announce Type: cross Abstract: Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR.
By Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang, Lingni Ma, Sebastian Starke, Robin Kips, Nadine Bertsch, Christian Holz, Federica Bogo