Recently, most of the state-of-the-art human pose estimation methods are
based on heatmap regression. The final coordinates of keypoints are obtained by
decoding heatmap directly. In this paper, we aim to find a better approach to
get more accurate localization results. We mainly put forward two suggestions
for improvement: 1) different features and methods should be applied for rough
and accurate localization, 2) relationship between keypoints should be
considered. Specifically, we propose a two-stage graph-based and model-agnostic
framework, called Graph-PCNN, with a localization subnet and a graph pose
refinement module added onto the original heatmap regression network. In the
first stage, heatmap regression network is applied to obtain a rough
localization result, and a set of proposal keypoints, called guided points, are
sampled. In the second stage, for each guided point, different visual feature
is extracted by the localization subnet. The relationship between guided points
is explored by the graph pose refinement module to get more accurate
localization results. Experiments show that Graph-PCNN can be used in various
backbones to boost the performance by a large margin. Without bells and
whistles, our best model can achieve a new state-of-the-art 76.8% AP on COCO
test-dev split.

本研究提出了一种基于图形回归的人体姿态估计框架，其中包含定位子网和图形姿势细化模块，通过不同的视觉特征提取以及探讨关键点之间的关系，显著提高了精度。在 COCO 数据集上，该模型达到了 76.8％的平均精度，达到了当前最新水平。