Graph convolutional networks (GCNs) have been the predominant methods in
skeleton-based human action recognition, including human-human interaction
recognition. However, when dealing with interaction sequences, current
GCN-based methods simply split the two-person skeleton into two discrete graphs
and perform graph convolution separately as done for single-person action
classification. Such operations ignore rich interactive information and hinder
effective spatial inter-body relationship modeling. To overcome the above
shortcoming, we introduce a novel unified two-person graph to represent
inter-body and intra-body correlations between joints. Experiments show
accuracy improvements in recognizing both interactions and individual actions
when utilizing the proposed two-person graph topology. In addition, We design
several graph labeling strategies to supervise the model to learn discriminant
spatial-temporal interactive features. Finally, we propose a two-person graph
convolutional network (2P-GCN). Our model achieves state-of-the-art results on
four benchmarks of three interaction datasets: SBU, interaction subsets of
NTU-RGB+D and NTU-RGB+D 120.

本文介绍了一种新型的二人图来表示关节之间的人与人交互和身体内部的相关性，并提出了两个人图卷积网络（2P-GCN）来识别人与人交互和个体动作。实验结果表明，该模型在 SBU、NTU-RGB+D 和 NTU-RGB+D 120 三个数据集的四项基准测试中取得了最先进的结果。