  Reconstructing 3D poses from 2D poses lacking depth information is
particularly challenging due to the complexity and diversity of human motion.
The key is to effectively model the spatial constraints between joints to
leverage their inherent dependencies. Thus, we propose a novel model, called
Double-chain Graph Convolutional Transformer (DC-GCT), to constrain the pose
through a double-chain design consisting of local-to-global and global-to-local
chains to obtain a complex representation more suitable for the current human
pose. Specifically, we combine the advantages of GCN and Transformer and design
a Local Constraint Module (LCM) based on GCN and a Global Constraint Module
(GCM) based on self-attention mechanism as well as a Feature Interaction Module
(FIM). The proposed method fully captures the multi-level dependencies between
human body joints to optimize the modeling capability of the model. Moreover,
we propose a method to use temporal information into the single-frame model by
guiding the video sequence embedding through the joint embedding of the target
frame, with negligible increase in computational cost. Experimental results
demonstrate that DC-GCT achieves state-of-the-art performance on two
challenging datasets (Human3.6M and MPI-INF-3DHP). Notably, our model achieves
state-of-the-art performance on all action categories in the Human3.6M dataset
using detected 2D poses from CPN, and our code is available at:
https://github.com/KHB1698/DC-GCT.
