  We study the problem of human action recognition using motion capture (MoCap)
sequences. Unlike existing techniques that take multiple manual steps to derive
standardized skeleton representations as model input, we propose a novel
Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences.
The model uses a hierarchical transformer with intra-frame off-set attention
and inter-frame self-attention. The attention mechanism allows the model to
freely attend between any two vertex patches to learn non-local relationships
in the spatial-temporal domain. Masked vertex modeling and future frame
prediction are used as two self-supervised tasks to fully activate the
bi-directional and auto-regressive attention in our hierarchical transformer.
The proposed method achieves state-of-the-art performance compared to
skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is
available at https://github.com/zgzxy001/STMT.
