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Our goal is to predict future video frames given a sequence of input frames.
Activity forecasting
K. M. Kitani, B. D. Ziebart, J. A. Bagnell, and M. Hebert · 2012
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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I. Sutskever, O. Vinyals, and Q. V. Le · 2014
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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S. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, G. E. Hinton, et al · 2016
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K. Greff, A. Rasmus, M. Berglund, T. Hao, H. Valpola, and J. Schmidhuber · 2016
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Later among the works it cites.
Video pixel networks
N. Kalchbrenner, A. v. d. Oord, K. Simonyan, I. Danihelka, O. Vinyals, A. Graves, and K. Kavukcuoglu · 2017
Later among the works it cites.
Hybrid vae: Improving deep generative models using partial observations
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Mocogan: Decomposing motion and content for video generation
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Decomposing motion and content for natural video sequence prediction
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Learning to generate long-term future via hierarchical prediction
R. Villegas, J. Yang, Y. Zou, S. Sohn, X. Lin, and H. Lee · 2017
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Generating the future with adversarial transformers
C. Vondrick and A. Torralba · 2017
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The pose knows: Video forecasting by generating pose futures
J. Walker, K. Marino, A. Gupta, and M. Hebert · 2017
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Sequential attend, infer, repeat: Generative modelling of moving objects
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