  This paper proposes a novel approach for predicting the motion of pedestrians
interacting with others. It uses a Generative Adversarial Network (GAN) to
sample plausible predictions for any agent in the scene. As GANs are very
susceptible to mode collapsing and dropping, we show that the recently proposed
Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory
prediction to avoid these issues. We also left out L2-loss in training the
generator, unlike some previous works, because it causes serious mode
collapsing though faster convergence.
  We show through experiments on real and synthetic data that the proposed
method leads to generate more diverse samples and to preserve the modes of the
predictive distribution. In particular, to prove this claim, we have designed a
toy example dataset of trajectories that can be used to assess the performance
of different methods in preserving the predictive distribution modes.
