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In this paper, we present a new bottom-up one-stage method for whole-bodypose estimation, which we call "hierarchical point regression," or HPRNet forshort. In standard body pose estimation, the locations of $\sim 17$ majorjoints on the human body are estimated. Differently, in whole-body poseestimation, the locations of fine-grained keypoints (68 on face, 21 on eachhand and 3 on each foot) are estimated as well, which creates a scale varianceproblem that needs to be addressed. To handle the scale variance amongdifferent body parts, we build a hierarchical point representation of bodyparts and jointly regress them. The relative locations of fine-grainedkeypoints in each part (e.g. face) are regressed in reference to the center ofthat part, whose location itself is estimated relative to the person center. Inaddition, unlike the existing two-stage methods, our method predicts whole-bodypose in a constant time independent of the number of people in an image. On theCOCO WholeBody dataset, HPRNet significantly outperforms all previous bottom-upmethods on the keypoint detection of all whole-body parts (i.e. body, foot,face and hand); it also achieves state-of-the-art results on face (75.4 AP) andhand (50.4 AP) keypoint detection. Code and models are available at\url{https://github.com/nerminsamet/HPRNet}.

HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation