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This paper introduces the unsupervised learning problem of playable video generation (PVG).
Measuring nominal scale agreement among many raters
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Auto-encoding variational bayes
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian SHI, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun WOO · 2015
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Anticipating the future by watching unlabeled video
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2015
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Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Recurrent environment simulators
Silvia Chiappa, Sébastien Racanière, Daan Wierstra, and Shakir Mohamed · 2017
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X. Lee, and S. Levine · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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Unsupervised learning of long-term motion dynamics for videos
Zelun Luo, Boya Peng, De-An Huang, Alexandre Alahi, and Li Fei-Fei · 2017
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Unsupervised action discovery and localization in videos
Khurram Soomro and Mubarak Shah · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Rainbow: Combining improvements in deep reinforcement learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Gheshlaghi Azar, and David Silver · 2018
Temporal cycle-consistency learning
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman · 2019
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Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, Andrea Vedaldi, and João F. Henriques · 2019
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Unsupervised keypoint learning for guiding class-conditional video prediction
Yunji Kim, Seonghyeon Nam, In Cho, and Seon Joo Kim · 2019
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Videoflow: A conditional flow-based model for stochastic video generation
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 2019
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Predicting future frames using retrospective cycle gan
Yong-Hoon Kwon and Min-Gyu Park · 2019
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First order motion model for image animation
Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe · 2019
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Stochastic adversarial video prediction
Alex X. Lee, Richard Zhang, Frederik Ebert, P. Abbeel, Chelsea Finn, and S. Levine · 2018
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Learning what you can do before doing anything
Oleh Rybkin, Karl Pertsch, Konstantinos G Derpanis, Kostas Daniilidis, and Andrew Jaegle · 2018
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Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
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Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Towards accurate generative models of video: A new metric & challenges
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2018
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Video-to-video synthesis
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
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Stochastic latent residual video prediction
Jean-Yves Franceschi, Edouard Delasalles, Mickael Chen, Sylvain Lamprier, and P. Gallinari · 2020
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Vid2game: Controllable characters extracted from real-world videos
Oran Gafni, Lior Wolf, and Yaniv Taigman · 2020
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Learning to Simulate Dynamic Environments with GameGAN
Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, and Sanja Fidler · 2020
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Transformation-based adversarial video prediction on large-scale data
Pauline Luc, A. Clark, S. Dieleman, D. Casas, Yotam Doron, Albin Cassirer, and K. Simonyan · 2020
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Action-conditioned benchmarking of robotic video prediction models: a comparative study
Manuel Serra Nunes, Atabak Dehban, Plinio Moreno, and José Santos-Victor · 2020
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Unsupervised video action clustering via motion-scene interaction constraint
B. Peng, J. Lei, H. Fu, C. Zhang, T. Chua, and X. Li · 2020
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Imaginator: Conditional spatio-temporal gan for video generation
Yaohui Wand, Piotr Bilinski, Francois Bremond, and Antitza Dantcheva · 2020
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2020
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Vid2player: Controllable video sprites that behave and appear like professional tennis players, 2020
Haotian Zhang, Cristobal Sciutto, Maneesh Agrawala, and Kayvon Fatahalian · 2020
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