Comparison of Hand Pose Estimation Performance Between Lightning Pose and DeepLabCut
Jamil Hanouneh · 2025
This paper presents a comparative study of two methods: Lightning Pose and DeepLabCut. Lightning Pose, a recent innovation, integrates advanced spatiotemporal models and semi-supervised learning to improve accuracy in dynamic settings. DeepLabCut, a widely used method, leverages deep learning techniques for pose estimation from video data. Our research aims to compare the performance of both methods in tracking various hand movements and gestures using a comprehensive dataset with multiple camera angles. The evaluation focuses on accuracy and consistency, quantified by the Euclidean distance between predicted keypoints and ground truth. Results indicate that DeepLabCut generally achieves lower Euclidean distances, reflecting higher accuracy but with greater variability in certain cases. In contrast, Lightning Pose, although slightly less accurate on average, demonstrates more consistent performance across diverse gestures and dynamic conditions due to its semi-supervised learning approach and spatiotemporal constraints. This study provides valuable insights into the strengths and limitations of each method, offering guidance for future research and applications in hand pose estimation.