  Trajectory prediction is confronted with the dilemma to capture the
multi-modal nature of future dynamics with both diversity and accuracy. In this
paper, we present a distribution discrimination (DisDis) method to predict
personalized motion patterns by distinguishing the potential distributions.
Motivated by that the motion pattern of each person is personalized due to
his/her habit, our DisDis learns the latent distribution to represent different
motion patterns and optimize it by the contrastive discrimination. This
distribution discrimination encourages latent distributions to be more
discriminative. Our method can be integrated with existing multi-modal
stochastic predictive models as a plug-and-play module to learn the more
discriminative latent distribution. To evaluate the latent distribution, we
further propose a new metric, probability cumulative minimum distance (PCMD)
curve, which cumulatively calculates the minimum distance on the sorted
probabilities. Experimental results on the ETH and UCY datasets show the
effectiveness of our method.
