用人体物理约束提升单目视频姿态估计精度，适合医疗健身应用。
Physics Informed Human Posture Estimation Based on 3D Landmarks from Monocular RGB-Videos
- 融合BlazePose与骨骼长度约束的加权优化算法
- 3D MPJPE降低10.2%，关节角度误差减少16.6%
- 轻量级后端处理，适配手机与笔记本
面向物理训练自动指导的应用日益普及，如物理治疗。这类应用依赖于单目视频流中的精确鲁棒姿态估计。当前最先进的模型如BlazePose在实时姿态追踪上表现优异，但缺乏解剖学约束，存在改进空间。本文提出一种实时后处理算法，融合BlazePose的3D与2D估计结果，通过加权优化惩罚偏离预期骨长和生物力学模型的偏差。利用卡尔曼滤波器根据个体解剖特征自适应调整测量置信度，实现骨长估计的精细化修正。在Physio2.2M数据集上的评估显示，相较BlazePose 3D估计，3D MPJPE降低10.2%，体段间角度误差下降16.6%。该方法基于计算高效的视频到3D姿态估计，提供解剖一致且鲁棒的姿态输出，适用于消费级笔记本与移动设备上的自动化理疗、健康监测及运动指导。算法在后端运行，仅处理匿名化数据。
原文摘要 · Abstract (English)
Applications providing automated coaching for physical training are increasing in popularity, for example physical therapy. These applications rely on accurate and robust pose estimation using monocular video streams. State-of-the-art models like BlazePose excel in real-time pose tracking, but their lack of anatomical constraints indicates improvement potential by including physical knowledge. We present a real-time post-processing algorithm fusing the strengths of BlazePose 3D and 2D estimations using a weighted optimization, penalizing deviations from expected bone length and biomechanical models. Bone length estimations are refined to the individual anatomy using a Kalman filter with adapting measurement trust. Evaluation using the Physio2.2M dataset shows a 10.2 percent reduction in 3D MPJPE and a 16.6 percent decrease in errors of angles between body segments compared to BlazePose 3D estimation. Our method provides a robust, anatomically consistent pose estimation based on a computationally efficient video-to-3D pose estimation, suitable for automated physiotherapy, healthcare, and sports coaching on consumer-level laptops and mobile devices. The refinement runs on the backend with anonymized data only.
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