Pose estimation has promised to impact healthcare by enabling more practical
methods to quantify nuances of human movement and biomechanics. However,
despite the inherent connection between pose estimation and biomechanics, these
disciplines have largely remained disparate. For example, most current pose
estimation benchmarks use metrics such as Mean Per Joint Position Error,
Percentage of Correct Keypoints, or mean Average Precision to assess
performance, without quantifying kinematic and physiological correctness - key
aspects for biomechanics. To alleviate this challenge, we develop OpenCapBench
to offer an easy-to-use unified benchmark to assess common tasks in human pose
estimation, evaluated under physiological constraints. OpenCapBench computes
consistent kinematic metrics through joints angles provided by an open-source
musculoskeletal modeling software (OpenSim). Through OpenCapBench, we
demonstrate that current pose estimation models use keypoints that are too
sparse for accurate biomechanics analysis. To mitigate this challenge, we
introduce SynthPose, a new approach that enables finetuning of pre-trained 2D
human pose models to predict an arbitrarily denser set of keypoints for
accurate kinematic analysis through the use of synthetic data. Incorporating
such finetuning on synthetic data of prior models leads to twofold reduced
joint angle errors. Moreover, OpenCapBench allows users to benchmark their own
developed models on our clinically relevant cohort. Overall, OpenCapBench
bridges the computer vision and biomechanics communities, aiming to drive
simultaneous advances in both areas.

通过 OpenCapBench 统一的基准评估，采用生理约束条件，使用肌肉骨骼建模软件（OpenSim）提供的关节角度计算一致的运动学指标，证明了当前姿势估计模型所使用的关键点在生物力学分析中过于稀疏，通过合成数据进行预训练模型的微调，能够预测出更密集的关键点，从而准确进行运动学分析，这样的微调可以减少二倍的关节角度误差。同时，OpenCapBench 还允许用户使用我们的临床相关数据集评估他们自己开发的模型，旨在推动计算机视觉和生物力学领域的同时进步。