Multi-Person Pose Estimation with Feature Enhancement and Decoupling Based on Contrastive Learning

Most methods of multi-person pose estimation (MPPE) treat the human detection and keypoint localization separately. They need additional supervision like instance bounding boxes, or complex hand-crafted processes like RoI cropping or grouping. In this article, we propose a novel one-stage MPPE method, named COPE, which unifies human detection and keypoint regression into an end-to-end learnable framework. To handle the challenges plague one-stage MPPE, i.e., instance overlapping and misalignment of local and global context, we design contrastive constraints at two levels of semantic granularity and feature sampling strategies. Based on a whole-process differentiable pipeline, COPE establishes a simple yet effective framework for MPPE without additional instance-level supervision and resource-intensive modules like transformer. Benefit from specially designed contrastive constraints and sampling strategies, COPE can better handle occluded scenes and correct keypoint localization errors. Extensive experiments demonstrate COPE’s superiority. It attains 71.3 AP and 18.0 FPS on COCO val2017, effectively balancing accuracy and speed. Particularly in crowded and occluded scenarios, COPE achieves state-of-the-art performance on CrowdPose and OCHuman, surpassing CID by 0.6 AP and 1.7 AP, respectively. Furthermore, COPE strongly improves generalization performance on the Human-Art benchmark, outperforming ED-Pose by 6.7 AP and ClickPose by 3.7 AP.