  Estimating a 3D human pose has proven to be a challenging task, primarily
because of the complexity of the human body joints, occlusions, and variability
in lighting conditions. In this paper, we introduce a higher-order graph
convolutional framework with initial residual connections for 2D-to-3D pose
estimation. Using multi-hop neighborhoods for node feature aggregation, our
model is able to capture the long-range dependencies between body joints.
Moreover, our approach leverages residual connections, which are integrated by
design in our network architecture, ensuring that the learned feature
representations retain important information from the initial features of the
input layer as the network depth increases. Experiments and ablations studies
conducted on two standard benchmarks demonstrate the effectiveness of our
model, achieving superior performance over strong baseline methods for 3D human
pose estimation.
