Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFormer constructs interaction graphs according to the semantic and distance correlations between the interactive body parts, and enhances the representation of each person by aggregating the information of the interactive body parts based on the learned graphs. Furthermore, we propose a Semantic Partition Module to transform each human skeleton sequence into a Body-Part-Time sequence to better capture the spatial and temporal information of the skeleton sequence for learning the graphs. Extensive experiments on three benchmark datasets demonstrate that our model outperforms the state-of-the-art with a significant margin.

本研究提出了一种新的基于骨架的交互作用识别模型Interaction Graph Transformer (IGFormer), 通过将交互体部建模为图形来实现交互作用的识别，并通过学习的图形聚合交互体部的信息以增强每个人的表示，其中还提出了一个称为Semantic Partition Module的模块来更好地捕捉骨架序列的空间和时间信息以学习图形。实验证明，该模型优于现有技术。

IGFormer:基于交互图转换器的基于骨架的人类交互识别