  In human pose estimation methods based on graph convolutional architectures,
the human skeleton is usually modeled as an undirected graph whose nodes are
body joints and edges are connections between neighboring joints. However, most
of these methods tend to focus on learning relationships between body joints of
the skeleton using first-order neighbors, ignoring higher-order neighbors and
hence limiting their ability to exploit relationships between distant joints.
In this paper, we introduce a higher-order regular splitting graph network
(RS-Net) for 2D-to-3D human pose estimation using matrix splitting in
conjunction with weight and adjacency modulation. The core idea is to capture
long-range dependencies between body joints using multi-hop neighborhoods and
also to learn different modulation vectors for different body joints as well as
a modulation matrix added to the adjacency matrix associated to the skeleton.
This learnable modulation matrix helps adjust the graph structure by adding
extra graph edges in an effort to learn additional connections between body
joints. Instead of using a shared weight matrix for all neighboring body
joints, the proposed RS-Net model applies weight unsharing before aggregating
the feature vectors associated to the joints in order to capture the different
relations between them. Experiments and ablations studies performed on two
benchmark datasets demonstrate the effectiveness of our model, achieving
superior performance over recent state-of-the-art methods for 3D human pose
estimation.
