Where Will They Go? Predicting Fine-Grained
Adversarial Multi-Agent Motion using
Conditional Variational Autoencoders
Abstract. Simultaneously and accurately forecasting the behavior of
many interacting agents is imperative for computer vision applications
to be widely deployed (e.g., autonomous vehicles, security, surveillance,
sports). In this paper, we present a technique using conditional variational autoencoder which learns a model that “personalizes” prediction to individual agent behavior within a group representation. Given
the volume of data available and its adversarial nature, we focus on
the sport of basketball and show that our approach efficiently predicts
context-specific agent motions. We find that our model generates results
that are three times as accurate as previous state of the art approaches
(5.74 ft vs. 17.95 ft)