Investigating Anthropometric Fidelity in SAM 3D Body

The recent release of SAM 3D Body \cite{sam3dbody2025} marks a significant milestone in human mesh recovery, demonstrating state-of-the-art performance in producing clean, topologically coherent meshes from single images. By leveraging the novel Momentum Human Rig (MHR), it achieves remarkable robustness to occlusion and diverse poses. However, our evaluation reveals a specific and consistent limitation: the model struggles to reconstruct detailed anthropometric deviations, especially on populations with special body shape alters 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 \textit{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 \enquote{regression to the mean} effect. We analyze these mechanisms to understand why individual biological details are smoothed out and propose specific, constructive pathways for future work to extend the impressive baseline performance of SAM 3D Body into the medical domain.