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Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale.
Actions as space-time shapes
Moshe Blank, Lena Gorelick, Eli Shechtman, Michal Irani, and Ronen Basri · 2005
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Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
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UCF101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Andrew M Saxe, James L McClelland, and Surya Ganguli · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
Marc’Aurelio Ranzato, Arthur Szlam, Joan Bruna, Michaël Mathieu, Ronan Collobert, and Sumit Chopra · 2014
Earlier work this paper cites.
Delving deeper into convolutional networks for learning video representations
Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using LSTMs
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Chainer: a next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
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Learning spatiotemporal features with 3D convolutional networks
Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri · 2015
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
Earlier work this paper cites.
Improved techniques for training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Rethinking the Inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Instance normalization: The missing ingredient for fast stylization
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
Earlier work this paper cites.
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
Earlier work this paper cites.
Quo vadis, action recognition? A new model and the Kinetics dataset
Joao Carreira and Andrew Zisserman · 2017
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Modulating early visual processing by language
Harm De Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville · 2017
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A learned representation for artistic style
Vincent Dumoulin, Jonathon Shlens, and Manjunath Kudlur · 2017
Earlier work this paper cites.
Self-supervised visual planning with temporal skip connections
Frederik Ebert, Chelsea Finn, Alex X Lee, and Sergey Levine · 2017
Cited alongside, same era.
Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Video pixel networks
Nal Kalchbrenner, Aäron van den Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
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, Mustafa Suleyman, and Andrew Zisserman · 2017
Cited alongside, same era.
Video generation from text
Yitong Li, Martin Renqiang Min, Dinghan Shen, David Carlson, and Lawrence Carin · 2018
Later among the works it cites.
cGANs with projection discriminator
Takeru Miyato and Masanori Koyama · 2018
Later among the works it cites.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Later among the works it cites.
Hierarchical video generation from orthogonal information: Optical flow and texture
Katsunori Ohnishi, Shohei Yamamoto, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Later among the works it cites.
TGANv2: Efficient training of large models for video generation with multiple subsampling layers
Masaki Saito and Shunta Saito · 2018
Later among the works it cites.
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Jae Hyun Lim and Jong Chul Ye · 2017
Cited alongside, same era.
Temporal generative adversarial nets with singular value clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito · 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.
Learning to generate long-term future via hierarchical prediction
Ruben Villegas, Jimei Yang, Yuliang Zou, Sungryull Sohn, Xunyu Lin, and Honglak Lee · 2017
Cited alongside, same era.
The pose knows: Video forecasting by generating pose futures
Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert · 2017
Cited alongside, same era.
Dance dance generation: Motion transfer for internet videos
Yipin Zhou, Zhaowen Wang, Chen Fang, Trung Bui, and Tamara L Berg · 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.
Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
Later among the works it cites.
Concetto Spampinato, Sergio Palazzo, P D’Oro, Francesca Murabito, Daniela Giordano, and M Shah · 2018
Later among the works it cites.
A two-stream variational adversarial network for video generation
Ximeng Sun, Huijuan Xu, and Kate Saenko · 2018
Later among the works it cites.
MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
Later among the works it cites.
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
Later among the works it cites.
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.
Group normalization
Yuxin Wu and Kaiming He · 2018
Later among the works it cites.
tempoGAN: A temporally coherent, volumetric GAN for super-resolution fluid flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey · 2018
Later among the works it cites.
Pose guided human video generation
Ceyuan Yang, Zhe Wang, Xinge Zhu, Chen Huang, Jianping Shi, and Dahua Lin · 2018
Later among the works it cites.
Self-attention generative adversarial networks
Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena · 2018
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Closest in time.
Kinetics
DeepMind, 2018 · 2019
Closest in time.
Cloud tpu
Google, 2018 · 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
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2019
Closest in time.
Generating diverse high-fidelity images with VQ-VAE-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
Closest in time.