  In this paper, we are interested in the bottom-up paradigm of estimating
human poses from an image. We study the dense keypoint regression framework
that is previously inferior to the keypoint detection and grouping framework.
Our motivation is that regressing keypoint positions accurately needs to learn
representations that focus on the keypoint regions.
  We present a simple yet effective approach, named disentangled keypoint
regression (DEKR). We adopt adaptive convolutions through pixel-wise spatial
transformer to activate the pixels in the keypoint regions and accordingly
learn representations from them. We use a multi-branch structure for separate
regression: each branch learns a representation with dedicated adaptive
convolutions and regresses one keypoint. The resulting disentangled
representations are able to attend to the keypoint regions, respectively, and
thus the keypoint regression is spatially more accurate. We empirically show
that the proposed direct regression method outperforms keypoint detection and
grouping methods and achieves superior bottom-up pose estimation results on two
benchmark datasets, COCO and CrowdPose. The code and models are available at
https://github.com/HRNet/DEKR.
