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:
this https URL

本文提出了一种名为 Double-chain Graph Convolutional Transformer (DC-GCT) 的新模型，通过局部到全局和全局到局部的双链设计来约束姿势，并结合 GCN 和 Transformer 的优势以及基于自注意机制的局部约束模块和全局约束模块以及特征交互模块来完整捕捉人体关节之间的多级依赖关系，将时间信息引入单帧模型，并在计算成本几乎不增加的情况下通过将目标帧的关节嵌入来指导视频序列嵌入。实验结果表明，DC-GCT 在两个具有挑战性的数据集（Human3.6M 和 MPI-INF-3DHP）上达到了最先进的性能。值得注意的是，我们的模型在 Human3.6M 数据集的所有动作类别上都使用了 CPN 检测到的 2D 姿势，我们的代码可在此 URL 获取。