Predicting crowd intentions and trajectories is critical for a range of real-world applications, involving social robotics and autonomous driving. Accurately modeling such behavior remains challenging due to the complexity of pairwise spatial-temporal interactions and the heterogeneous influence of groupwise dynamics. To address these challenges, we propose Hyper-STTN, a Hypergraph-based Spatial-Temporal Transformer Network for crowd trajectory prediction. Hyper-STTN constructs multiscale hypergraphs of varying group sizes to model groupwise correlations, captured through spectral hypergraph convolution based on random-walk probabilities. In parallel, a spatial-temporal transformer is employed to learn pedestrians' pairwise latent interactions across spatial and temporal dimensions. These heterogeneous groupwise and pairwise features are subsequently fused and aligned via a multimodal transformer. Extensive experiments on public pedestrian motion datasets demonstrate that Hyper-STTN consistently outperforms state-of-the-art baselines and ablation models.
@article{arxiv.2401.06344,
title = {Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction},
author = {Weizheng Wang and Baijian Yang and Sungeun Hong and Wenhai Sun and Byung-Cheol Min},
journal= {arXiv preprint arXiv:2401.06344},
year = {2026}
}