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Generating videos is a complex task that is accomplished by generating a set of temporally coherent images frame-by-frame.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2006
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Modeling video evolution for action recognition
Basura Fernando, Efstratios Gavves, Jose Oramas M, Amir Ghodrati, and Tinne Tuytelaars · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhutdinov · 2015
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Dynamic image networks for action recognition
Hakan Bilen, Basura Fernando, Efstratios Gavves, Andrea Vedaldi, and Stephen Gould · 2016
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Video frame interpolation via adaptive separable convolution
Simon Niklaus, Long Mai, and Feng Liu · 2017
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Language modeling with recurrent highway hypernetworks
Joseph Suarez · 2017
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Mocogan: Decomposing motion and content for video generation
S. Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2017
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Frame-recurrent video super-resolution
Mehdi SM Sajjadi, Raviteja Vemulapalli, and Matthew Brown · 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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Efficient video generation on complex datasets
Aidan Clark, Jeff Donahue, and Karen Simonyan · 2019
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Tgan: Deep tensor generative adversarial nets for large image generation
Zihan Ding, Xiao-Yang Liu, Miao Yin, and Linghe Kong · 2019
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Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aäron van den Oord, and Oriol Vinyals · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhoefer, and Gordon Wetzstein · 2019
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Continual learning with hypernetworks
Johannes Von Oswald, Christian Henning, João Sacramento, and Benjamin F Grewe · 2019
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Edvr: Video restoration with enhanced deformable convolutional networks
Xintao Wang, Kelvin C. K. Chan, K. Yu, Chao Dong, and Chen Change Loy · 2019
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iSeeBetter: Spatio-temporal video super-resolution using recurrent generative back-projection networks
Aman Chadha, John Britto, and M. Mani Roja · 2020
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Learning temporal coherence via self-supervision for GAN-based video generation
Mengyu Chu, You Xie, Jonas Mayer, Laura Leal-Taixé , and Nils Thuerey · 2020
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Light field networks: Neural scene representations with single-evaluation rendering
Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand · 2021
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Adversarial generation of continuous images
Ivan Skorokhodov, Savva Ignatyev, and Mohamed Elhoseiny · 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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Jacob Walker, Ali Razavi, and Aäron van den Oord · 2021
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Pauline Luc, Aidan Clark, Sander Dieleman, Diego de Las Casas, Yotam Doron, Albin Cassirer, and Karen Simonyan · 2020
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Softmax splatting for video frame interpolation
Simon Niklaus and Feng Liu · 2020
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Video frame interpolation without temporal priors
Youjian Zhang, Chaoyue Wang, and Dacheng Tao · 2020
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Nerv: Neural representations for videos
Hao Chen, Bo He, Hanyu Wang, Yixuan Ren, Ser Nam Lim, and Abhinav Shrivastava · 2021
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How2sign: a large-scale multimodal dataset for continuous american sign language
Amanda Duarte, Shruti Palaskar, Lucas Ventura, Deepti Ghadiyaram, Kenneth DeHaan, Florian Metze, Jordi Torres, and Xavier Giro-i Nieto · 2021
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Model compression via hyper-structure network, 2021
Shangqian Gao, Feihu Huang, and Heng Huang · 2021
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Ad-nerf: Audio driven neural radiance fields for talking head synthesis
Yudong Guo, Keyu Chen, Sen Liang, Yong-Jin Liu, Hujun Bao, and Juyong Zhang · 2021
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Videogpt: Video generation using vq-vae and transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, and Aravind Srinivas · 2021
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Gan prior embedded network for blind face restoration in the wild
Tao Yang, Peiran Ren, Xuansong Xie, and Lei Zhang · 2021
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Videoinr: Learning video implicit neural representation for continuous space-time super-resolution
Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang · 2022
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Stylizing 3d scene via implicit representation and hypernetwork
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Vrt: A video restoration transformer
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Fast conditional network compression using bayesian hypernetworks
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Hypershot: Few-shot learning by kernel hypernetworks
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Generating videos with dynamics-aware implicit generative adversarial networks
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