  Understanding crowd motion dynamics is critical to real-world applications,
e.g., surveillance systems and autonomous driving. This is challenging because
it requires effectively modeling the socially aware crowd spatial interaction
and complex temporal dependencies. We believe attention is the most important
factor for trajectory prediction. In this paper, we present STAR, a
Spatio-Temporal grAph tRansformer framework, which tackles trajectory
prediction by only attention mechanisms. STAR models intra-graph crowd
interaction by TGConv, a novel Transformer-based graph convolution mechanism.
The inter-graph temporal dependencies are modeled by separate temporal
Transformers. STAR captures complex spatio-temporal interactions by
interleaving between spatial and temporal Transformers. To calibrate the
temporal prediction for the long-lasting effect of disappeared pedestrians, we
introduce a read-writable external memory module, consistently being updated by
the temporal Transformer. We show that with only attention mechanism, STAR
achieves state-of-the-art performance on 5 commonly used real-world pedestrian
prediction datasets.
