The typical bottom-up human pose estimation framework includes two stages,
keypoint detection and grouping. Most existing works focus on developing
grouping algorithms, e.g., associative embedding, and pixel-wise keypoint
regression that we adopt in our approach. We present several schemes that are
rarely or unthoroughly studied before for improving keypoint detection and
grouping (keypoint regression) performance. First, we exploit the keypoint
heatmaps for pixel-wise keypoint regression instead of separating them for
improving keypoint regression. Second, we adopt a pixel-wise spatial
transformer network to learn adaptive representations for handling the scale
and orientation variance to further improve keypoint regression quality. Last,
we present a joint shape and heatvalue scoring scheme to promote the estimated
poses that are more likely to be true poses. Together with the tradeoff heatmap
estimation loss for balancing the background and keypoint pixels and thus
improving heatmap estimation quality, we get the state-of-the-art bottom-up
human pose estimation result. Code is available at
this https URL

本文介绍了一种基于向下拼接的人体姿态估计框架，其中通过关键点检测和聚类两个阶段进行处理。在该框架下，本文提出了多种方案，包括使用关键点热图进行像素级别的关键点回归、采用像素级别空间变换网络学习适应性表示处理尺度和方向差异并提高关键点回归质量，以及使用联合形状和热值评分方案提升预测姿态的可靠性，最终获得了最先进的人体底部向上姿态估计结果。