DSPose: Dual-Space-Driven Keypoint Topology Modeling for Human Pose Estimation.

Human pose estimation is the basis of many downstream tasks, such as motor intervention, behavior understanding, and human-computer interaction. The existing human pose estimation methods rely too much on the similarity of keypoints at the image feature level, which is vulnerable to three problems: object occlusion, keypoints ghost, and neighbor pose interference. We propose a dual-space-driven topology model for the human pose estimation task.