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We present a video generation model that accurately reproduces object motion, changes in camera viewpoint, and new content that arises over time.
Markov processes over denumerable products of spaces, describing large systems of automata
Leonid Nisonovich Vaserstein · 1969
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Nonrecursive digital filter design using the i_0-sinh window function
James F Kaiser · 1974
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Video textures
Arno Schödl, Richard Szeliski, David H. Salesin, and Irfan Essa · 2000
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Dynamic textures
Gianfranco Doretto, Alessandro Chiuso, Ying Nian Wu, and Stefano Soatto · 2003
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Edge-avoiding À-trous wavelet transform for fast global illumination filtering
Holger Dammertz, Daniel Sewtz, Johannes Hanika, and Hendrik P. A. Lensch · 2010
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Neural networks for machine learning lecture 6a overview of mini-batch gradient descent
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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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 · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
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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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Video super-resolution with convolutional neural networks
Armin Kappeler, Seunghwan Yoo, Qiqin Dai, and Aggelos K Katsaggelos · 2016
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Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Quo vadis, action recognition? a new model and the kinetics dataset
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Silvia Chiappa, Sébastien Racaniere, Daan Wierstra, and Shakir Mohamed · 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 Oord, Karen Simonyan, Ivo Danihelka, Oriol Vinyals, Alex Graves, and Koray Kavukcuoglu · 2017
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Temporal generative adversarial nets with singular value clipping
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Detail-revealing deep video super-resolution
Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiaya Jia · 2017
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Towards high resolution video generation with progressive growing of sliced wasserstein gans
Dinesh Acharya, Zhiwu Huang, Danda Pani Paudel, and Luc Van Gool · 2018
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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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Voxceleb2: Deep speaker recognition
Joon Son Chung, Arsha Nagrani, and Andrew Zisserman · 2018
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David Ha and Jürgen Schmidhuber · 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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Stochastic adversarial video prediction
Alex X Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, and Sergey Levine · 2018
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Which training methods for gans do actually converge?
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2018
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Faceforensics: A large-scale video dataset for forgery detection in human faces
Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner · 2018
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Mead: A large-scale audio-visual dataset for emotional talking-face generation
Kaisiyuan Wang, Qianyi Wu, Linsen Song, Zhuoqian Yang, Wayne Wu, Chen Qian, Ran He, Yu Qiao, and Chen Change Loy · 2020
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Dtvnet: Dynamic time-lapse video generation via single still image
Jiangning Zhang, Chao Xu, Liang Liu, Mengmeng Wang, Xia Wu, Yong Liu, and Yunliang Jiang · 2020
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Differentiable augmentation for data-efficient gan training
Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, and Song Han · 2020
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Stylevideogan: A temporal generative model using a pretrained stylegan
Gereon Fox, Ayush Tewari, Mohamed Elgharib, and Christian Theobalt · 2021
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Alias-free generative adversarial networks
Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2021
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Frame-recurrent video super-resolution
Mehdi S. M. Sajjadi, Raviteja Vemulapalli, and Matthew Brown · 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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Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks
Wei Xiong, Wenhan Luo, Lin Ma, Wei Liu, and Jiebo Luo · 2018
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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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Recurrent back-projection network for video super-resolution
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita · 2019
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Drivegan: Towards a controllable high-quality neural simulation
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Infinite nature: Perpetual view generation of natural scenes from a single image
Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa · 2021
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Image super-resolution via iterative refinement
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A good image generator is what you need for high-resolution video synthesis
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One-shot free-view neural talking-head synthesis for video conferencing
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Videogpt: Video generation using vq-vae and transformers
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Cascaded diffusion models for high fidelity image generation
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The role of imagenet classes in fréchet inception distance
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Generating videos with dynamics-aware implicit generative adversarial networks
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