  Predicting crowded intents and trajectories is crucial in varouls real-world
applications, including service robots and autonomous vehicles. Understanding
environmental dynamics is challenging, not only due to the complexities of
modeling pair-wise spatial and temporal interactions but also the diverse
influence of group-wise interactions. To decode the comprehensive pair-wise and
group-wise interactions in crowded scenarios, we introduce Hyper-STTN, a
Hypergraph-based Spatial-Temporal Transformer Network for crowd trajectory
prediction. In Hyper-STTN, crowded group-wise correlations are constructed
using a set of multi-scale hypergraphs with varying group sizes, captured
through random-walk robability-based hypergraph spectral convolution.
Additionally, a spatial-temporal transformer is adapted to capture pedestrians'
pair-wise latent interactions in spatial-temporal dimensions. These
heterogeneous group-wise and pair-wise are then fused and aligned though a
multimodal transformer network. Hyper-STTN outperformes other state-of-the-art
baselines and ablation models on 5 real-world pedestrian motion datasets.
