研究 SAM 3D Body 的人体测量保真度问题

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Abstract:The release of SAM 3D Body is a recent development in human mesh recovery, demonstrating improved performance in producing clean, topologically coherent meshes from single images. By leveraging the Momentum Human Rig (MHR), it achieves robustness to occlusion and diverse poses. However, our evaluation reveals a specific and consistent limitation: the model struggles to reconstruct detailed anthropometric deviations, particularly in populations exhibiting distinctive morphological alterations such as geriatric muscle atrophy, scoliosis, or pregnancy, even when these features are prominent in the input image. In this paper, we investigate this phenomenon not as a failure of the model's capacity, but as a byproduct of the "perception-distortion trade-off". We posit that the architectural reliance on the low-dimensional parametric MHR representation, combined with semantic-invariant conditioning (DINOv3) and annotation-based alignment, creates a pervasive "regression to the mean" effect. We analyze these mechanisms to understand why individual biological details are smoothed out. Furthermore, we state our contributions by proposing specific, constructive pathways for future work, such as implicit-explicit hybrid representations and Medical-in-the-Loop alignment, to extend the baseline performance of SAM 3D Body into the high-precision medical domain.

Subjects:	Graphics (cs.GR); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET)
Cite as:	arXiv:2601.06035 [cs.GR]
	(or arXiv:2601.06035v2 [cs.GR] for this version)
	https://doi.org/10.48550/arXiv.2601.06035 arXiv-issued DOI via DataCite

Submission history

From: Aizierjiang Aiersilan [view email]
[v1] Tue, 2 Dec 2025 05:33:17 UTC (8,806 KB)
[v2] Sat, 2 May 2026 00:45:24 UTC (8,835 KB)