Markerless 3D movement analysis from monocular video enables accessible biomechanical assessment in clinical and sports settings. However, most research-grade pipelines rely on GPU acceleration, limiting deployment on consumer-grade hardware and in low-resource environments. In this work, we optimize a monocular 3D biomechanics pipeline derived from the MonocularBiomechanics framework for efficient CPU-only execution. Through profiling-driven system optimization, including model initialization restructuring, elimination of disk I/O serialization, and improved CPU parallelization. Experiments on a consumer workstation (AMD Ryzen 7 9700X CPU) show a 2.47x increase in processing throughput and a 59.6\% reduction in total runtime, with initialization latency reduced by 4.6x. Despite these changes, biomechanical outputs remain highly consistent with the baseline implementation (mean joint-angle deviation 0.35$^\circ$, $r=0.998$). These results demonstrate that research-grade vision-based biomechanics pipelines can be deployed on commodity CPU hardware for scalable movement assessment.

翻译：从单目视频进行无标记3D运动分析，使得在临床和运动场景中实现可及性生物力学评估成为可能。然而，大多数研究级管道依赖GPU加速，限制了其在消费级硬件和低资源环境中的部署。本研究优化了源自MonocularBiomechanics框架的单目3D生物力学管道，以实现高效的仅CPU执行。通过基于性能分析的系统优化，包括模型初始化重构、消除磁盘I/O序列化以及改进CPU并行化，在消费级工作站（AMD Ryzen 7 9700X CPU）上的实验表明：处理吞吐量提升2.47倍，总运行时间降低59.6%，初始化延迟减少4.6倍。尽管进行了这些更改，生物力学输出仍与基准实现保持高度一致性（平均关节角度偏差0.35°，相关系数r=0.998）。这些结果表明，研究级视觉生物力学管道可部署于商用CPU硬件，用于可扩展的运动评估。