  Predicting pedestrian movement is critical for human behavior analysis and
also for safe and efficient human-agent interactions. However, despite
significant advancements, it is still challenging for existing approaches to
capture the uncertainty and multimodality of human navigation decision making.
In this paper, we propose SocialVAE, a novel approach for human trajectory
prediction. The core of SocialVAE is a timewise variational autoencoder
architecture that exploits stochastic recurrent neural networks to perform
prediction, combined with a social attention mechanism and a backward posterior
approximation to allow for better extraction of pedestrian navigation
strategies. We show that SocialVAE improves current state-of-the-art
performance on several pedestrian trajectory prediction benchmarks, including
the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement
dataset. Code is available at: https://github.com/xupei0610/SocialVAE.
