  Multi-person motion prediction is a challenging task, especially for
real-world scenarios of highly interacted persons. Most previous works have
been devoted to studying the case of weak interactions (e.g., walking
together), in which typically forecasting each human pose in isolation can
still achieve good performances. This paper focuses on collaborative motion
prediction for multiple persons with extreme motions and attempts to explore
the relationships between the highly interactive persons' pose trajectories.
Specifically, a novel cross-query attention (XQA) module is proposed to
bilaterally learn the cross-dependencies between the two pose sequences
tailored for this situation. A proxy unit is additionally introduced to bridge
the involved persons, which cooperates with our proposed XQA module and subtly
controls the bidirectional spatial information flows. These designs are then
integrated into a Transformer-based architecture and the resulting model is
called Proxy-bridged Game Transformer (PGformer) for multi-person interactive
motion prediction. Its effectiveness has been evaluated on the challenging ExPI
dataset, which involves highly interactive actions. Our PGformer consistently
outperforms the state-of-the-art methods in both short- and long-term
predictions by a large margin. Besides, our approach can also be compatible
with the weakly interacted CMU-Mocap and MuPoTS-3D datasets and extended to the
case of more than 2 individuals with encouraging results.

Computer Vision and Pattern Recognition