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Reconstructing 3D poses from 2D poses lacking depth information isparticularly challenging due to the complexity and diversity of human motion.The key is to effectively model the spatial constraints between joints toleverage their inherent dependencies. Thus, we propose a novel model, calledDouble-chain Graph Convolutional Transformer (DC-GCT), to constrain the posethrough a double-chain design consisting of local-to-global and global-to-localchains to obtain a complex representation more suitable for the current humanpose. Specifically, we combine the advantages of GCN and Transformer and designa 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 betweenhuman body joints to optimize the modeling capability of the model. Moreover,we propose a method to use temporal information into the single-frame model byguiding the video sequence embedding through the joint embedding of the targetframe, with negligible increase in computational cost. Experimental resultsdemonstrate that DC-GCT achieves state-of-the-art performance on twochallenging datasets (Human3.6M and MPI-INF-3DHP). Notably, our model achievesstate-of-the-art performance on all action categories in the Human3.6M datasetusing detected 2D poses from CPN, and our code is available at:https://github.com/KHB1698/DC-GCT.

Double-chain Constraints for 3D Human Pose Estimation in Images and Videos