Stochastic Video Generation with a Learned Prior

Emily Denton, Rob Fergus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Generating video frames that accurately predict future world states is challenging. Existing ap-proaches either fail to capture the full distribution of outcomes, or yield blurry generations, or both. In this paper we introduce a video generation model with a learned prior over stochastic latent variables at each time step. Video frames are generated by drawing samples from this prior and combining them with a deterministic estimate of the future frame. The approach is simple and easily trained end-to-end on a variety of datascts. Sample generations are both varied and sharp, even many frames into the future, and compare favorably to those from existing approaches.

Original languageEnglish (US)
Title of host publication35th International Conference on Machine Learning, ICML 2018
EditorsAndreas Krause, Jennifer Dy
PublisherInternational Machine Learning Society (IMLS)
Pages1906-1919
Number of pages14
ISBN (Electronic)9781510867963
StatePublished - 2018
Event35th International Conference on Machine Learning, ICML 2018 - Stockholm, Sweden
Duration: Jul 10 2018Jul 15 2018

Publication series

Name35th International Conference on Machine Learning, ICML 2018
Volume3

Other

Other35th International Conference on Machine Learning, ICML 2018
Country/TerritorySweden
CityStockholm
Period7/10/187/15/18

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software

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