用单目视频无训练生成人体生物力学数据，可家用评估运动状态
SAM4Dcap: Training-free Biomechanical Twin System from Monocular Video
- 结合4D人体网格与OpenSim模拟器，直接从单视频输出生物力学参数
- 步行和跳落测试中膝关节运动预测接近多视角系统水平
- 无需训练、开源易用，适合临床与家庭场景的运动分析
定量生物力学分析对临床诊断和损伤预防至关重要，但常受限于光学运动捕捉系统的高昂成本。尽管多视角视频降低了门槛，但在家庭场景中仍需单目拍摄。本文提出SAM4Dcap，一个开源、端到端框架，可在不额外训练的情况下，从单目视频估计生物力学指标。该框架整合了SAM-Body4D的时序一致4D人体网格重建与OpenSim生物力学求解器，将重建网格转换为兼容多种肌肉骨骼模型的轨迹文件。我们引入自动化提示策略和原生Linux构建流程以支持处理。初步评估显示，在步行与跳落任务中，SAM4Dcap在膝关节运动学预测上接近多视角系统表现，但髋屈曲和残余抖动仍存在差异。通过融合先进计算机视觉与成熟生物力学仿真，SAM4Dcap为非实验室环境下的运动分析提供了灵活且可及的基础。
原文摘要 · Abstract (English)
Quantitative biomechanical analysis is essential for clinical diagnosis and injury prevention but is often restricted to laboratories due to the high cost of optical motion capture systems. While multi-view video approaches have lowered barriers, they remain impractical for home-based scenarios requiring monocular capture. This paper presents SAM4Dcap, an open-source, end-to-end framework for estimating biomechanical metrics from monocular video without additional training. SAM4Dcap integrates the temporally consistent 4D human mesh recovery of SAM-Body4D with the OpenSim biomechanical solver. The pipeline converts reconstructed meshes into trajectory files compatible with diverse musculoskeletal models. We introduce automated prompting strategies and a Linux-native build for processing. Preliminary evaluations on walking and drop-jump tasks indicate that SAM4Dcap has the potential to achieve knee kinematic predictions comparable to multi-view systems, although some discrepancies in hip flexion and residual jitter remain. By bridging advanced computer vision with established biomechanical simulation, SAM4Dcap provides a flexible, accessible foundation for non-laboratory motion analysis.
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