FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments
Aoru Xue, Yiming Ren, Zining Song, Mao Ye, Xinge Zhu, Yuexin Ma
Abstract
We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matching module that predicts the alignment among each sensor, even in the absence of calibration. Additionally, our coarse-to-fine sensor-expandable pose optimizer further optimizes the 3D human key points and the alignments, it is also capable of incorporating additional cameras to enhance accuracy. Extensive experiments on Human-M3 and FreeMotion datasets demonstrate that our method significantly outperforms state-of-the-art single-modal methods, offering an expandable and efficient solution for multi-person motion capture across various applications.
BibTeX
@article{Xue_Ren_Song_Ye_Zhu_Ma_2025, title={FreeCap: Hybrid Calibration-Free Motion Capture in Open Environments}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32977}, DOI={10.1609/aaai.v39i9.32977}, abstractNote={We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matching module that predicts the alignment among each sensor, even in the absence of calibration. Additionally, our coarse-to-fine sensor-expandable pose optimizer further optimizes the 3D human key points and the alignments, it is also capable of incorporating additional cameras to enhance accuracy. Extensive experiments on Human-M3 and FreeMotion datasets demonstrate that our method significantly outperforms state-of-the-art single-modal methods, offering an expandable and efficient solution for multi-person motion capture across various applications.}, number={9}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xue, Aoru and Ren, Yiming and Song, Zining and Ye, Mao and Zhu, Xinge and Ma, Yuexin}, year={2025}, month={Apr.}, pages={9032-9040} }