  Pedestrian trajectory prediction is challenging due to its uncertain and
multimodal nature. While generative adversarial networks can learn a
distribution over future trajectories, they tend to predict out-of-distribution
samples when the distribution of future trajectories is a mixture of multiple,
possibly disconnected modes. To address this issue, we propose a
multi-generator model for pedestrian trajectory prediction. Each generator
specializes in learning a distribution over trajectories routing towards one of
the primary modes in the scene, while a second network learns a categorical
distribution over these generators, conditioned on the dynamics and scene
input. This architecture allows us to effectively sample from specialized
generators and to significantly reduce the out-of-distribution samples compared
to single generator methods.
