TY - GEN
T1 - Stochastic Video Generation with a Learned Prior
AU - Denton, Emily
AU - Fergus, Rob
N1 - Publisher Copyright:
© 2018 35th International Conference on Machine Learning, ICML 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85057280496
T3 - 35th International Conference on Machine Learning, ICML 2018
SP - 1906
EP - 1919
BT - 35th International Conference on Machine Learning, ICML 2018
A2 - Krause, Andreas
A2 - Dy, Jennifer
PB - International Machine Learning Society (IMLS)
T2 - 35th International Conference on Machine Learning, ICML 2018
Y2 - 10 July 2018 through 15 July 2018
ER -