Hierarchical Graph Networks for 3D Human Pose Estimation
Abstract
Recent 2D-to-3D human pose estimation works tend to utilize the graph structure formed by the topology of the human skeleton. However, we argue that this skeletal topology is too sparse to reflect the body structure and suffer from serious 2D-to-3D ambiguity problem. To overcome these weaknesses, we propose a novel graph convolution network architecture, Hierarchical Graph Networks (HGN). It is based on denser graph topology generated by our multi-scale graph structure building strategy, thus providing more delicate geometric information. The proposed architecture contains three sparse-to-fine representation subnetworks organized in parallel, in which multi-scale graph-structured features are processed and exchange information through a novel feature fusion strategy, leading to rich hierarchical representations. We also introduce a 3D coarse mesh constraint to further boost detail-related feature learning. Extensive experiments demonstrate that our HGN achieves the state-of-the art performance with reduced network parameters. Code is released at https://github.com/qingshi9974/BMVC2021-Hierarchical-Graph-Networks-for-3D-Human-Pose-Estimation.
Cite
@article{arxiv.2111.11927,
title = {Hierarchical Graph Networks for 3D Human Pose Estimation},
author = {Han Li and Bowen Shi and Wenrui Dai and Yabo Chen and Botao Wang and Yu Sun and Min Guo and Chenlin Li and Junni Zou and Hongkai Xiong},
journal= {arXiv preprint arXiv:2111.11927},
year = {2023}
}
Comments
accepted by BMVC 2021