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
this https URL

本文提出一种名为 “解离的关键点回归” 的有效方法，采用自适应卷积通过像素级空间变换激活关键点附近的像素并相应地从中学习特征向量，该方法在人体姿态估计上体现出良好的性能。