CVMay 16

Markerless Motion Capture for Biomechanical Whole-Body Kinematic Estimation in Infants

arXiv:2605.171208.8
Predicted impact top 67% in CV · last 90 daysOriginality Synthesis-oriented
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For clinicians and researchers assessing infant motor development, this work provides a preliminary benchmark for markerless motion capture, though it is incremental due to applying existing methods to a new population.

This study evaluated three pose estimation frameworks on infant videos, finding that SAM 3D Body achieved the best 3D kinematic reconstruction (19–28 mm error) and could distinguish clinically relevant movement patterns, while Sapiens had the lowest reprojection error (22.8 pixels).

arly identification of motor impairment in infancy relies on expert visual assessment of spontaneous movement, motivating the development of automated, objective alternatives. One promising approach is using computer vision, which benefits from high quality pose estimation from video. In this study, we systematically evaluated three state-of-the-art pose estimation frameworks (MeTRAbs-ACAE, SAM 3D Body, and Sapiens) on 100 videos over 13 sessions of 8 infants recorded with a multi-view markerless motion capture system. We quantified keypoint detection accuracy using reprojection error, geometric consistency, and Procrustes-aligned 3D position error, and demonstrated proof-of-concept for fitting an inverse kinematic framework to infant data. While Sapiens achieved the lowest reprojection error and highest geometric consistency of the methods evaluated (22.8 pixels and 0.82, respectively), SAM 3D Body provided the most comprehensive 3D information for kinematic reconstruction with Procrustes-aligned position errors of 19 to 28 mm. We demonstrate in a case comparison example that biomechanical models fit to SAM 3D estimates distinguish representative movement patterns in infants related to motor development, as identified by a clinical expert. Together, these findings highlight both the promise and current limitations of 3D pose estimation for infant biomechanics and establish preliminary groundwork for scalable, video-based assessment of early motor development.

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