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Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users — arXiv:2609.18406

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.18406
License (as archived): CC0-1.0
Archived: 2026-10-15T16:16:29.309Z
SHA-256 of the archived text: c2b5882c411990f30202091d98002156d83a51eef20ec2632c1c25a1dd6241b3
Stable URL of this page: https://metraipolis.com/documento/dc_ea7771fc8fd3

How to cite

Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users — arXiv:2609.18406 — arXiv (en), https://arxiv.org/abs/2609.18406 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea7771fc8fd3 (Metraipolis archive dc_ea7771fc8fd3, sha256 c2b5882c411990f30202091d98002156d83a51eef20ec2632c1c25a1dd6241b3)

BibTeX

@misc{dcea7771fc8fd3,
  title = {Prosthesis-Aware 3D Human Pose Estimation: A Dataset and Benchmark for RSP Users — arXiv:2609.18406},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.18406},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea7771fc8fd3; sha256 c2b5882c411990f30202091d98002156d83a51eef20ec2632c1c25a1dd6241b3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.18406. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recovering 3D human body motion from video is important for applications such as rehabilitation assessment and sports performance evaluation. For prosthesis users, this requires capturing both natural body joints and the geometry of the prosthetic device, a challenge that existing methods are not designed to address. Model-based estimators rely on body models trained on non-amputee individuals and cannot represent prosthesis geometry, while model-free methods lack body kinematic priors and are unreliable under occlusion. This challenge is particularly prominent for users of running-specific prostheses (RSPs), where the RSP has a complex curved geometry and moves dynamically during exercise. To fill this gap, we collect RSP3D, the first 3D dataset of RSP users, covering essential daily-life and exercise actions from participants with varied amputation conditions, using a multi-camera marker-based motion capture setup. We formally define the task of prosthesis-aware 3D pose estimation, evaluate representative methods in a zero-shot setting, and confirm their individual limitations. We further propose a hybrid baseline combining model-based body joint estimation with model-free RSP shape recovery, establishing a starting point for future research.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.18406 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-15T15:45:19.784Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).