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We present a framework for video modeling based on denoising diffusion probabilistic models that produces long-duration video completions in a variety of realistic environments.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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A recurrent latent variable model for sequential data
Junyoung Chung, Kyle Kastner, Laurent Dinh, Kratarth Goel, Aaron C Courville, and Yoshua Bengio · 2015
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Deep residual learning for image recognition. arxiv 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Memory-efficient backpropagation through time
Audrunas Gruslys, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex Graves · 2016
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H Campbell, and Sergey Levine · 2017
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Carla: An open urban driving simulator
Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Unbiasing truncated backpropagation through time
Corentin Tallec and Yann Ollivier · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Earlier work this paper cites.
Deep variational reinforcement learning for pomdps
Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, and Shimon Whiteson · 2018
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Self-attention with relative position representations
Peter Shaw, Jakob Uszkoreit, and Ashish Vaswani · 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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Hierarchical long-term video prediction without supervision
Ruben Villegas, Dumitru Erhan, Honglak Lee, et al · 2018
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Hierarchical Long-term Video Prediction without Supervision
Nevan Wichers, Ruben Villegas, Dumitru Erhan, and Honglak Lee · 2018
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Fitvid: Overfitting in pixel-level video prediction
Mohammad Babaeizadeh, Mohammad Taghi Saffar, Suraj Nair, Sergey Levine, Chelsea Finn, and Dumitru Erhan · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Symbolic music generation with diffusion models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Clockwork variational autoencoders
Vaibhav Saxena, Jimmy Ba, and Danijar Hafner · 2021
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Adversarial video generation on complex datasets
Aidan Clark, Jeff Donahue, and Karen Simonyan · 2019
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Model-based reinforcement learning for atari
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Variational temporal abstraction
Taesup Kim, Sungjin Ahn, and Yoshua Bengio · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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Very deep vaes generalize autoregressive models and can outperform them on images
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Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
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Rethinking and improving relative position encoding for vision transformer
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Video generative adversarial networks: a review
Nuha Aldausari, Arcot Sowmya, Nadine Marcus, and Gelareh Mohammadi · 2022
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Diffusion probabilistic modeling for video generation
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