PoseGuru: Landmarks for Explainable Pose Correction using Exemplar-Guided Algorithmic Recourse

Human pose correction is crucial in fields such as fitness, rehabilitation, and sports. Despite recent advancements, existing approaches often lack explainable, personalized, and fine-grained corrections. We present PoseGuru, an explainable optimization-based approach, leveraging algorithmic recourse and counterfactuals to iteratively refine pose landmarks by minimizing classification loss, enforcing user-specific anatomical constraints, and precisely aligning with the target pose. Our method is simple, interpretable, and adaptable, enabling easy incorporation of applicationspecific constraints. For robust evaluation, we introduce two new datasets, YogaHPC and Pilates32+P, generated by biomechanically perturbing landmarks of correct poses. PoseGuru consistently outperformed existing methods on both datasets, as assessed using metrics such as MPIJAD and PCIK. Furthermore, a comprehensive user study involving yoga and pilates experts confirmed PoseGuru's effectiveness, highlighting its capacity to facilitate user-driven pose correction across diverse pose types. Overall, PoseGuru provides an explainable and personalized solution suitable for critical applications in fitness and rehabilitation.