Bi-Level Keypoint Relation Helps Versatile and Occluded Human Pose Estimation

Recently, there has been significant progress in 2D pose estimation. However, accurately localizing limb keypoints and occluded keypoints is still challenging. To tackle these difficulties, prior in human body structure has been leveraged in previous studies. One approach involves localizing a challenging keypoint by utilizing its neighbor keypoint. A previous study successfully employed neighbor-joint spatial relation (SR), which transfers features from a neighbor keypoint to the target keypoint being predicted. Building upon this idea, our work extends the keypoint relation-based method by incorporating another level of keypoint relation, namely channel-wise feature relation. This additional feature relation (FR) module assists in selecting more suitable neighbor keypoint feature channels and enhances the effectiveness of SR. By combining FR and SR, we develop a simple and intuitive bi-level keypoint relation module that can be trained end-to-end with existing methods. Through comprehensive experimental results and ablation studies, we demonstrate the effectiveness of our approach.