想从视频直接算出关节力矩、地面反作用力？BioModule能直接插在现成的姿态估计器后面，不用改模型就给生物力学预测，跑分还覆盖了七个主流方法。
BioModule是一个轻量级插件式时间变换器，可附加在任何3D姿态估计器之后，从标准17关节点3D骨架预测生物力学属性。研究构建了Human3.6M与Human3.6Mplus的对齐数据集，实现帧级跨模态监督。在七个SOTA 3D姿态估计器上进行了系统性基准测试，首次分析上游姿态质量对下游生物力学预测保真度的影响。BioModule无需修改上游模型，为康复、运动科学等应用提供了可扩展的物理可解释运动分析方案。
Pose-to-Biomechanics: Bridging 3D Human Pose Estimation and Biomechanical Attribute Prediction
Recent progress in 3D human pose estimation has made markerless recovery of skeletal motion increasingly accurate and scalable. However, most pose estimators remain optimized for geometric keypoint accuracy, while many real-world applications in rehabilitation, sports science, ergonomics, and clinical movement analysis require biomechanical quantities that describe how the body moves, loads, and activates. In this work, we propose BioModule, a lightweight plug-in temporal transformer that attaches downstream of any 3D pose estimator and predicts biomechanical attributes from standard 17-joint 3D skeletons. BioModule is estimator-agnostic and requires no modification of the upstream pose model, enabling existing pose estimators to be extended toward physically interpretable motion analysis. To train and evaluate BioModule, we construct a large-scale aligned dataset pairing Human3.6M video and 3D keypoints with the biomechanical label space of Human3.6Mplus. We establish and verify anatomical correspondence between coordinate systems of the two datasets, enabling frame-accurate cross-modal supervision. Using this aligned supervision, BioModule predicts biomechanical quantities. We further benchmark BioModule across seven state-of-the-art 3D pose estimators, providing the first systematic analysis of how upstream pose estimation quality propagates to downstream biomechanical prediction fidelity. The results position BioModule as a compact, modular bridge between vision-based pose estimation and biomechanically meaningful human motion analysis.