  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.
