  Graph convolutional networks (GCNs) have proven to be an effective approach
for 3D human pose estimation. By naturally modeling the skeleton structure of
the human body as a graph, GCNs are able to capture the spatial relationships
between joints and learn an efficient representation of the underlying pose.
However, most GCN-based methods use a shared weight matrix, making it
challenging to accurately capture the different and complex relationships
between joints. In this paper, we introduce an iterative graph filtering
framework for 3D human pose estimation, which aims to predict the 3D joint
positions given a set of 2D joint locations in images. Our approach builds upon
the idea of iteratively solving graph filtering with Laplacian regularization
via the Gauss-Seidel iterative method. Motivated by this iterative solution, we
design a Gauss-Seidel network (GS-Net) architecture, which makes use of weight
and adjacency modulation, skip connection, and a pure convolutional block with
layer normalization. Adjacency modulation facilitates the learning of edges
that go beyond the inherent connections of body joints, resulting in an
adjusted graph structure that reflects the human skeleton, while skip
connections help maintain crucial information from the input layer's initial
features as the network depth increases. We evaluate our proposed model on two
standard benchmark datasets, and compare it with a comprehensive set of strong
baseline methods for 3D human pose estimation. Our experimental results
demonstrate that our approach outperforms the baseline methods on both
datasets, achieving state-of-the-art performance. Furthermore, we conduct
ablation studies to analyze the contributions of different components of our
model architecture and show that the skip connection and adjacency modulation
help improve the model performance.
