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Generative adversarial models (GANs) continue to produce advances in terms of the visual quality of still images, as well as the learning of temporal correlations.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Generating videos with scene dynamics, October 2016
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Wasserstein GAN, December 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Unsupervised learning of disentangled representations from video
Emily L Denton and Vighnesh Birodkar · 2017
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Improved training of Wasserstein GANs
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 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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Temporal generative adversarial nets with singular value clipping
M. Saito, E. Matsumoto, and S. Saito · 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
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Towards high resolution video generation with progressive growing of sliced Wasserstein GANs, 2018
Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, and Luc Van Gool · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 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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MoCoGAN: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
Adversarial video generation on complex datasets, 2019
Aidan Clark, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Generating high fidelity images with subscale pixel network and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2019
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Towards accurate generative models of video: A new metric & challenges, 2019
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphael Marinier, Marcin Michalski, and Sylvain Gelly · 2019
Cited alongside, same era.
Markov Decision Process for video generation
V. Yushchenko, N. Araslanov, and S. Roth · 2019
StyleRig: Rigging StyleGAN for 3D control over portrait images
Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zöllhofer, and Christian Theobalt · 2020
Later among the works it cites.
Scaling autoregressive video models
Dirk Weissenborn, Jakob Uszkoreit, and Oscar Täckström · 2020
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Coherence and identity learning for arbitrary-length face video generation
Shuquan Ye, Chu Han, Jiaying Lin, Han Guoqiang, and Shengfeng He · 2020
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Transforming and projecting images to class-conditional generative networks
Minyoung Huh, Jun-Yan Zhu Richard Zhang, Sylvain Paris, and Aaron Hertzmann · 2020
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Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal GAN
Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi · 2020
Later among the works it cites.
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Cited alongside, same era.
Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Cited alongside, same era.
CrowdGAN: Identity-free interactive crowd video generation and beyond
L. Chai, Y. Liu, W. Liu, G. Han, and S. He · 2020
Cited alongside, same era.
GANSpace: Discovering Interpretable GAN Controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
Cited alongside, same era.
Lower dimensional kernels for video discriminators
Emmanuel Kahembwe and Subramanian Ramamoorthy · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Face identity disentanglement via latent space mapping
Yotam Nitzan, Amit Bermano, Yangyan Li, and Daniel Cohen-Or · 2020
Cited alongside, same era.
Kibeom Hong, Youngjung Uh, and Hyeran Byun · 2021
Closest in time.
Playable video generation
Willi Menapace, Stephane Lathuiliere, Sergey Tulyakov, Aliaksandr Siarohin, and Elisa Ricci · 2021
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Temporal shift GAN for large scale video generation
Andres Munoz, Mohammadreza Zolfaghari, Max Argus, and Thomas Brox · 2021
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Encoding in style: a StyleGAN encoder for image-to-image translation
Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or · 2021
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A good image generator is what you need for high-resolution video synthesis
Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, and Sergey Tulyakov · 2021
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InMoDeGAN: Interpretable motion decomposition generative adversarial network for video generation, 2021
Yaohui Wang, Francois Bremond, and Antitza Dantcheva · 2021
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Temporal shift GAN for large scale video generation
Andres Munoz, Mohammadreza Zolfaghari, Max Argus, and Thomas Brox · 2021
Closest in time.
A good image generator is what you need for high-resolution video synthesis
Yu Tian, Jian Ren, Menglei Chai, Kyle Olszewski, Xi Peng, Dimitris N. Metaxas, and Sergey Tulyakov · 2021
Closest in time.
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
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