Accurate baseball player pose refinement using motion prior guidance

Abstract

Human pose estimation (HPE) is challenging due to the need to accurately capture rapid and occluded body movements, often resulting in uncertain predictions. In the context of fast sports actions like baseball swings, existing HPE methods insufficiently leverage domain-specific prior knowledge about these movements. To address this gap, we propose the Baseball Player Pose Corrector (BPPC), an optimization framework that utilizes high-quality 3D standard motion data to refine 2D keypoints in baseball swing videos. BPPC operates in two stages: first, it aligns the 3D standard motion to test swing videos through action recognition, offset learning, and 3D-to-2D projection. Next, it applies movement-aware optimization to refine the keypoints, ensuring robustness to variations in swing patterns. Notably, BPPC does not rely on additional datasets; it only requires manually annotated 3D standard motion data for baseball swings. Experimental results demonstrate that BPPC improves keypoint estimation accuracy by up to 2.4% on a baseball swing dataset, particularly enhancing keypoints with confidence scores below 0.5. Qualitative analysis further highlights BPPC’s ability to correct rapidly moving joints, such as elbows and wrists.