  Accurate 3D human pose estimation is a challenging task due to occlusion and
depth ambiguity. In this paper, we introduce a multi-hop graph transformer
network designed for 2D-to-3D human pose estimation in videos by leveraging the
strengths of multi-head self-attention and multi-hop graph convolutional
networks with disentangled neighborhoods to capture spatio-temporal
dependencies and handle long-range interactions. The proposed network
architecture consists of a graph attention block composed of stacked layers of
multi-head self-attention and graph convolution with learnable adjacency
matrix, and a multi-hop graph convolutional block comprised of multi-hop
convolutional and dilated convolutional layers. The combination of multi-head
self-attention and multi-hop graph convolutional layers enables the model to
capture both local and global dependencies, while the integration of dilated
convolutional layers enhances the model's ability to handle spatial details
required for accurate localization of the human body joints. Extensive
experiments demonstrate the effectiveness and generalization ability of our
model, achieving competitive performance on benchmark datasets.
