Current works on multi-person 3D pose estimation mainly focus on the
estimation of the 3D joint locations relative to the root joint and ignore the
absolute locations of each pose. In this paper, we propose the Human Depth
Estimation Network (HDNet), an end-to-end framework for absolute root joint
localization in the camera coordinate space. Our HDNet first estimates the 2D
human pose with heatmaps of the joints. These estimated heatmaps serve as
attention masks for pooling features from image regions corresponding to the
target person. A skeleton-based Graph Neural Network (GNN) is utilized to
propagate features among joints. We formulate the target depth regression as a
bin index estimation problem, which can be transformed with a soft-argmax
operation from the classification output of our HDNet. We evaluate our HDNet on
the root joint localization and root-relative 3D pose estimation tasks with two
benchmark datasets, i.e., Human3.6M and MuPoTS-3D. The experimental results
show that we outperform the previous state-of-the-art consistently under
multiple evaluation metrics. Our source code is available at:
this https URL

本文提出了一个名为 HDNet 的绝对根关节定位的端到端框架，首先使用关节点的热图估计 2D 人类姿势，并使用这些估计热图作为池化特征和通过骨架图神经网络在关节之间传播特征，并将目标深度回归公式化为一个 bin 索引估计问题，实验结果表明 HDNet 在多个评估指标下始终优于先前的最新技术。