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The video generation task can be formulated as a prediction of future video frames given some past frames.
Video (language) modeling: a baseline for generative models of natural videos
MarcAurelio Ranzato, Arthur Szlam, Joan Bruna, Michael Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Spatio-temporal video autoencoder with differentiable memory
Viorica Patraucean, Ankur Handa, and Roberto Cipolla · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
SHI Xingjian, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Earlier work this paper cites.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Pixelsnail: An improved autoregressive generative model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2017
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Unsupervised learning of disentangled representations from video
Emily L Denton et al · 2017
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Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
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Efficient softmax approximation for gpus
Edouard Grave, Armand Joulin, Moustapha Cissé, Hervé Jégou, et al · 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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Video pixel networks
Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2017
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The kinetics human action video dataset
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al · 2017
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Temporal generative adversarial nets with singular value clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, et al · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Towards high resolution video generation with progressive growing of sliced wasserstein gans
Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, and Luc Van Gool · 2018
Cited alongside, same era.
Adaptive input representations for neural language modeling
Alexei Baevski and Michael Auli · 2018
Cited alongside, same era.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
Cited alongside, same era.
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Guilin Liu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Later among the works it cites.
Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning
Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jianmin Wang, and Philip S Yu · 2018
Later among the works it cites.
Eidetic 3d lstm: A model for video prediction and beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, and Li Fei-Fei · 2018
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Adversarial video generation on complex datasets
A Clark, J Donahue, and K Simonyan · 2019
Later among the works it cites.
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Contextvp: Fully context-aware video prediction
Wonmin Byeon, Qin Wang, Rupesh Kumar Srivastava, and Petros Koumoutsakos · 2018
Cited alongside, same era.
A short note about kinetics-600
Joao Carreira, Eric Noland, Andras Banki-Horvath, Chloe Hillier, and Andrew Zisserman · 2018
Cited alongside, same era.
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
Cited alongside, same era.
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
Cited alongside, same era.
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
Cited alongside, same era.
Learning to decompose and disentangle representations for video prediction
Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, and Juan Carlos Niebles · 2018
Cited alongside, same era.
Fast decoding in sequence models using discrete latent variables
Łukasz Kaiser, Aurko Roy, Ashish Vaswani, Niki Parmar, Samy Bengio, Jakob Uszkoreit, and Noam Shazeer · 2018
Cited alongside, same era.
Yuki Endo, Yoshihiro Kanamori, and Shigeru Kuriyama · 2019
Later among the works it cites.
Axial attention in multidimensional transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans · 2019
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Videoflow: A flow-based generative model for video
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, and Durk Kingma · 2019
Later among the works it cites.
End-to-end time-lapse video synthesis from a single outdoor image
Seonghyeon Nam, Chongyang Ma, Menglei Chai, William Brendel, Ning Xu, and Seon Joo Kim · 2019
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Video generation from single semantic label map
Junting Pan, Chengyu Wang, Xu Jia, Jing Shao, Lu Sheng, Junjie Yan, and Xiaogang Wang · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
Later among the works it cites.
Singan: Learning a generative model from a single natural image
Tamar Rott Shaham, Tali Dekel, and Tomer Michaeli · 2019
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Few-shot video-to-video synthesis
Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, and Bryan Catanzaro · 2019
Later among the works it cites.
Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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An empirical study of spatial attention mechanisms in deep networks
Xizhou Zhu, Dazhi Cheng, Zheng Zhang, Stephen Lin, and Jifeng Dai · 2019
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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Transformation-based adversarial video prediction on large-scale data
Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan · 2020
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Feature quantization improves gan training
Yang Zhao, Chunyuan Li, Ping Yu, Jianfeng Gao, and Changyou Chen · 2020
Closest in time.