{
  "id": 13608,
  "url": "https://arxiv.org/abs/2607.22534v1",
  "title": "SM4RT: Learning Structured Motion Geometry for 4D Reconstruction",
  "summary": "Geometry Foundation Models (GFMs) have substantially advanced monocular 3D reconstruction, yet extending this capability to 4D dynamic understanding remains a fundamental challenge. Most existing motion perception methods (e.g., sparse tracking, dense point-wise flow) treat motion as independent point-wise displacements, ignoring the structured nature of physical motion. However, real-world objects usually obey rigid-body kinematics, and points thus usually move collectively, not in isolation. M",
  "authors": "Shing Ho J. Lin, Wenzhao Zheng, Dong Zhuo, Yuqi Wu, Jie Zhou, Jiwen Lu",
  "category": "research",
  "topics": "privacy-surveillance",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T17:59:51.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/13608",
  "original_url": "https://arxiv.org/abs/2607.22534v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}