  Skeleton-based human action recognition has achieved a great interest in
recent years, as skeleton data has been demonstrated to be robust to
illumination changes, body scales, dynamic camera views, and complex
background. Nevertheless, an effective encoding of the latent information
underlying the 3D skeleton is still an open problem. In this work, we propose a
novel Spatial-Temporal Transformer network (ST-TR) which models dependencies
between joints using the Transformer self-attention operator. In our ST-TR
model, a Spatial Self-Attention module (SSA) is used to understand intra-frame
interactions between different body parts, and a Temporal Self-Attention module
(TSA) to model inter-frame correlations. The two are combined in a two-stream
network which outperforms state-of-the-art models using the same input data on
both NTU-RGB+D 60 and NTU-RGB+D 120.
