Zuo, Chengxu, Wang, Yiming, Zhan, Lishuang, Guo, Shihui, Yi, Xinyu, Xu, Feng and Qin, Yipeng  ORCID: https://orcid.org/0000-0002-1551-9126
2024.
Loose inertial poser: Motion capture with IMU-attached loose-wear jacket.
Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024,
Seattle, USA,
17-21 June 2024.
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
IEEE,
pp. 2209-2219.
10.1109/CVPR52733.2024.00215
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Abstract
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. Our dataset and code are available at: https://github.com/ZuoCX1966/Loose-Inertial-Poser
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Date Type: | Published Online |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | IEEE |
| ISBN: | 9798350353013 |
| ISSN: | 1063-6919 |
| Date of First Compliant Deposit: | 20 August 2025 |
| Date of Acceptance: | 27 February 2024 |
| Last Modified: | 20 Aug 2025 16:02 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/167381 |
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