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Video generation is an inherently challenging task, as it requires modeling realistic temporal dynamics as well as spatial content.
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Deep multi-scale video prediction beyond mean square error
M. Mathieu, C. Couprie, and Y. LeCun · 2016
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Optical flow estimation using a spatial pyramid network
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Temporal generative adversarial nets with singular value clipping
M. Saito, E. Matsumoto, and S. Saito · 2017
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Stochastic video generation with a learned prior
E. Denton and R. Fergus · 2018
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Probabilistic video generation using holistic attribute control
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Inferring semantic layout for hierarchical text-to-image synthesis
S. Hong, D. Yang, J. Choi, and H. Lee · 2018
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A style-based generator architecture for generative adversarial networks
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Stochastic adversarial video prediction
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Mocogan: Decomposing motion and content for video generation
S. Tulyakov, M.-Y. Liu, X. Yang, and J. Kautz · 2018
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Activation maximization generative adversarial nets
Z. Zhou, H. Cai, S. Rong, Y. Song, K. Ren, W. Zhang, Y. Yu, and J. Wang · 2018
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