  Prior work on human motion forecasting has mostly focused on predicting the
future motion of single subjects in isolation from their past pose sequence. In
the presence of closely interacting people, however, this strategy fails to
account for the dependencies between the different subject's motions. In this
paper, we therefore introduce a motion prediction framework that explicitly
reasons about the interactions of two observed subjects. Specifically, we
achieve this by introducing a pairwise attention mechanism that models the
mutual dependencies in the motion history of the two subjects. This allows us
to preserve the long-term motion dynamics in a more realistic way and more
robustly predict unusual and fast-paced movements, such as the ones occurring
in a dance scenario. To evaluate this, and because no existing motion
prediction datasets depict two closely-interacting subjects, we introduce the
LindyHop600K dance dataset. Our results evidence that our approach outperforms
the state-of-the-art single person motion prediction techniques.
