Adversarial Generative Grammars for Human Activity Prediction

AJ Piergiovanni; Anelia Angelova; Alexander Toshev; Michael S. Ryoo

ECCV 2020poster36 citations

Adversarial Generative Grammars for Human Activity Prediction

AJ Piergiovanni, Anelia Angelova, Alexander Toshev, Michael S. Ryoo

Abstract

In this paper we propose an adversarial generative grammar model for future prediction. The objective is to learn a model that explicitly captures temporal dependencies, providing a capability to forecast multiple, distinct future activities. Our adversarial grammar is designed so that it can learn stochastic production rules from the data distribution, jointly with its latent non-terminal representations. Being able to select multiple production rules during inference leads to different predicted outcomes, thus efficiently modeling many plausible futures.

BibTeX

@inproceedings{eccv2020_adversarialgener,
  title = {Adversarial Generative Grammars for Human Activity Prediction},
  author = {AJ Piergiovanni and Anelia Angelova and Alexander Toshev and Michael S. Ryoo},
  booktitle = {ECCV 2020},
  year = {2020}
}