In this paper, we present a new bottom-up one-stage method for whole-body pose estimation, which we name "hierarchical point regression," or HPRNet for short, referring to the network that implements this method. To handle the scale variance among different body parts, we build a hierarchical point representation of body parts and jointly regress them. Unlike the existing two-stage methods, our method predicts whole-body pose in a constant time independent of the number of people in an image. On the COCO WholeBody dataset, HPRNet significantly outperforms all previous bottom-up methods on the keypoint detection of all whole-body parts (i.e. body, foot, face and hand); it also achieves state-of-the-art results in the face (75.4 AP) and hand (50.4 AP) keypoint detection. Code and models are available at https://github.com/nerminsamet/HPRNet.git.

翻译：在本文中,我们提出了一个新的自下而上一阶段的全体估计方法,我们用“等级点回归”或HPRNet来命名,简称为“等级点回归”,简称HPRNet,指采用这种方法的网络。为了处理不同身体部分之间的比例差异,我们建立了身体部分的等级代表,并共同倒退它们。与现有的两阶段方法不同,我们的方法预测整个身体在固定的时间里构成,而与图像中的人数无关。在COCO 整体点回归数据集中,HPRNet大大超过以前所有全体部分(即身体、脚、脸和手)关键点探测方法的所有自下而上的方法;它还实现了面部(75.4 AP)和手(50.4 AP)关键点检测的状态结果。代码和模型可在https://github.com/nerminsamet/HPRNet.git上查阅。