Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket

Chengxu Zuo, Yiming Wang, Lishuang Zhan, Shihui Guo, Xinyu Yi, Feng Xu, Yipeng Qin; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 2209-2219

Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating four Inertial Measurement Units (IMUs) into a loose-wear jacket. Specifically we address the challenge of scarce loose-wear IMU training data by proposing a Secondary Motion AutoEncoder (SeMo-AE) that learns to model and synthesize the effects of secondary motion between the skin and loose clothing on IMU data. SeMo-AE is leveraged to generate a diverse synthetic dataset of loose-wear IMU data to augment training for the pose estimation network and significantly improve its accuracy. For validation we collected a dataset with various subjects and 2 wearing styles (zipped and unzipped). Experimental results demonstrate that our approach maintains high-quality real-time posture estimation even in loose-wear scenarios.