Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915855752757248 |
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| author | Forte, Maria-Paola Athanasiou, Nikos Ballardini, Giulia Bartels, Jan Ulrich Kuchenbecker, Katherine J. Black, Michael J. |
| author_facet | Forte, Maria-Paola Athanasiou, Nikos Ballardini, Giulia Bartels, Jan Ulrich Kuchenbecker, Katherine J. Black, Michael J. |
| contents | Capturing accurate 3D human pose in the wild would provide valuable data for training pose estimation and motion generation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face. In contrast, wearable bioimpedance sensing can cheaply and unobtrusively measure ground-truth skin-to-skin contact. Consequently, we propose a novel framework that combines visual pose estimators with bioimpedance sensing to capture the 3D pose of people by taking self-contact into account. Our method, BioTUCH, initializes the pose using an off-the-shelf estimator and introduces contact-aware pose optimization during measured self-contact: reprojection error and deviations from the input estimate are minimized while enforcing vertex proximity constraints. We validate our approach using a new dataset of synchronized RGB video, bioimpedance measurements, and 3D motion capture. Testing with three input pose estimators, we demonstrate an average of 11.7% improvement in reconstruction accuracy. We also present a miniature wearable bioimpedance sensor that enables efficient large-scale collection of contact-aware training data for improving pose estimation and generation using BioTUCH. Code and data are available at biotuch.is.tue.mpg.de |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04862 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing Forte, Maria-Paola Athanasiou, Nikos Ballardini, Giulia Bartels, Jan Ulrich Kuchenbecker, Katherine J. Black, Michael J. Computer Vision and Pattern Recognition Capturing accurate 3D human pose in the wild would provide valuable data for training pose estimation and motion generation methods. While video-based estimation approaches have become increasingly accurate, they often fail in common scenarios involving self-contact, such as a hand touching the face. In contrast, wearable bioimpedance sensing can cheaply and unobtrusively measure ground-truth skin-to-skin contact. Consequently, we propose a novel framework that combines visual pose estimators with bioimpedance sensing to capture the 3D pose of people by taking self-contact into account. Our method, BioTUCH, initializes the pose using an off-the-shelf estimator and introduces contact-aware pose optimization during measured self-contact: reprojection error and deviations from the input estimate are minimized while enforcing vertex proximity constraints. We validate our approach using a new dataset of synchronized RGB video, bioimpedance measurements, and 3D motion capture. Testing with three input pose estimators, we demonstrate an average of 11.7% improvement in reconstruction accuracy. We also present a miniature wearable bioimpedance sensor that enables efficient large-scale collection of contact-aware training data for improving pose estimation and generation using BioTUCH. Code and data are available at biotuch.is.tue.mpg.de |
| title | Contact-Aware Refinement of Human Pose Pseudo-Ground Truth via Bioimpedance Sensing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.04862 |