{
  "id": 6623,
  "url": "https://arxiv.org/abs/2604.03299v1",
  "title": "MoViD: View-Invariant 3D Human Pose Estimation via Motion-View Disentanglement",
  "summary": "3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require large amounts of training data, and suffer from high inference latency. We propose MoViD, a viewpoint-invariant 3D human pose estimation framework that disentangles viewpoint information from motion fea",
  "authors": "Yejia Liu, Hengle Jiang, Haoxian Liu, Runxi Huang, Xiaomin Ouyang",
  "category": "research",
  "topics": "healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-29T09:08:57.000Z",
  "fetched_at": "2026-07-14T16:32:37.310Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6623",
  "original_url": "https://arxiv.org/abs/2604.03299v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}