Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation

In keypoint estimation tasks such as human pose estimation, heatmap-based regression is the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer from quantization error and require excessive computation to generate and post-process. Motivated to find a more efficient solution, we propose a new heatmap-free keypoint estimation method in which individual keypoints and sets of spatially related keypoints (i.e., poses) are modeled as objects within a dense single-stage anchor-based detection framework. Hence, we call our method KAPAO (pronounced "Ka-Pow!") for Keypoints And Poses As Objects. We apply KAPAO to the problem of single-stage multi-person human pose estimation by simultaneously detecting human pose objects and keypoint objects and fusing the detections to exploit the strengths of both object representations. In experiments, we observe that KAPAO is significantly faster and more accurate than previous methods, which suffer greatly from heatmap post-processing. Moreover, the accuracy-speed trade-off is especially favourable in the practical setting when not using test-time augmentation. Our large model, KAPAO-L, achieves an AP of 70.6 on the Microsoft COCO Keypoints validation set without test-time augmentation while being 2.5x faster than the next best single-stage model, whose accuracy is 4.0 AP less. Furthermore, KAPAO excels in the presence of heavy occlusion. On the CrowdPose test set, KAPAO-L achieves new state-of-the-art accuracy for a single-stage method with an AP of 68.9.