  Multi-person motion prediction remains a challenging problem, especially in
the joint representation learning of individual motion and social interactions.
Most prior methods only involve learning local pose dynamics for individual
motion (without global body trajectory) and also struggle to capture complex
interaction dependencies for social interactions. In this paper, we propose a
novel Social-Aware Motion Transformer (SoMoFormer) to effectively model
individual motion and social interactions in a joint manner. Specifically,
SoMoFormer extracts motion features from sub-sequences in displacement
trajectory space to effectively learn both local and global pose dynamics for
each individual. In addition, we devise a novel social-aware motion attention
mechanism in SoMoFormer to further optimize dynamics representations and
capture interaction dependencies simultaneously via motion similarity
calculation across time and social dimensions. On both short- and long-term
horizons, we empirically evaluate our framework on multi-person motion datasets
and demonstrate that our method greatly outperforms state-of-the-art methods of
single- and multi-person motion prediction. Code will be made publicly
available upon acceptance.
