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.

翻译：提供自动化体能训练指导的应用（例如物理治疗）日益普及。这些应用依赖于使用单目视频流进行准确且鲁棒的姿态估计。尽管BlazePose等先进模型在实时姿态跟踪方面表现出色，但其缺乏解剖学约束，表明通过融入物理知识存在改进潜力。我们提出一种实时后处理算法，通过加权优化融合BlazePose三维与二维估计的优势，对偏离预期骨骼长度和生物力学模型的情况进行惩罚。骨骼长度估计通过采用自适应测量信任度的卡尔曼滤波器，针对个体解剖结构进行细化。使用Physio2.2M数据集的评估显示，相较于BlazePose三维估计，本方法在三维MPJPE上降低了10.2%，在身体节段间角度误差上减少了16.6%。我们的方法基于计算高效的视频到三维姿态估计流程，提供了鲁棒且解剖学一致的姿态估计，适用于消费级笔记本电脑和移动设备上的自动化物理治疗、医疗保健和运动指导。细化过程仅在后端使用匿名化数据运行。