用视频重建人体三维模型，无标记精准分析步态参数。
Biomechanically Accurate Gait Analysis: A 3d Human Reconstruction Framework for Markerless Estimation of Gait Parameters
- 通过视频重建人体三维姿态，提取类运动捕捉的生物力学标志点
- 与标记数据对比，步态时空与关节运动参数高度一致
- 适合临床和真实场景中的无标记步态分析应用
本文提出一种基于视频数据的3D人体重建框架，用于无标记步态分析。与传统关键点方法不同，该方法提取类运动捕捉系统中具有生物力学意义的标志点，并将其集成至OpenSim中进行关节运动学估计。为评估性能，对步态的时空参数与运动学参数进行了分析，结果表明与基于标记的数据具有强一致性，显著优于仅使用姿态估计的方法。该框架具备可扩展性、无标记性和可解释性，支持视觉生物力学在临床及真实场景中的广泛应用。
原文摘要 · Abstract (English)
This paper presents a biomechanically interpretable framework for gait analysis using 3D human reconstruction from video data. Unlike conventional keypoint based approaches, the proposed method extracts biomechanically meaningful markers analogous to motion capture systems and integrates them within OpenSim for joint kinematic estimation. To evaluate performance, both spatiotemporal and kinematic gait parameters were analysed against reference marker-based data. Results indicate strong agreement with marker-based measurements, with considerable improvements when compared with pose-estimation methods alone. The proposed framework offers a scalable, markerless, and interpretable approach for accurate gait assessment, supporting broader clinical and real world deployment of vision based biomechanics
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。