SM4RT: Learning Structured Motion Geometry for 4D Reconstruction
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
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ethics.ai (24 July 2026), “SM4RT: Learning Structured Motion Geometry for 4D Reconstruction,” evidence record 13608, https://ethics.ai/record/13608 (originally published by arXiv).
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