  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.
