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Our work explores temporal self-supervision for GAN-based video generation tasks.
Rank analysis of incomplete block designs: I. The method of paired comparisons
Ralph Allan Bradley and Milton E Terry. 1952 · 1952
Earlier work this paper cites.
Two-frame motion estimation based on polynomial expansion. In Scandinavian conference on Image analysis . Springer, 363–370
Gunnar Farnebäck. 2003 · 2003
Earlier work this paper cites.
A Bayesian approach to adaptive video super resolution. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on . IEEE, 209–216
Ce Liu and Deqing Sun. 2011 · 2011
Earlier work this paper cites.
Generative adversarial nets. In Advances in neural information processing systems . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman. 2014 · 2014
Earlier work this paper cites.
Flownet: Learning optical flow with convolutional networks. In Proceedings of the IEEE international conference on computer vision . 2758–2766
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Hausser, Caner Hazirbas, Vladimir Golkov, Patrick Van Der Smagt, Daniel Cremers, and Thomas Brox. 2015 · 2015
Earlier work this paper cites.
Video super-resolution via deep draft-ensemble learning. In Proceedings of the IEEE International Conference on Computer Vision . 531–539
Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, and Jiaya Jia. 2015 · 2015
Earlier work this paper cites.
Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision . Springer, 694–711
Justin Johnson, Alexandre Alahi, and Li Fei-Fei. 2016 · 2016
Earlier work this paper cites.
Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition . 1646–1654
Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. 2016 · 2016
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2016
Earlier work this paper cites.
Artistic style transfer for videos. In German Conference on Pattern Recognition . Springer, 26–36
Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016 · 2016
Earlier work this paper cites.
Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 1874–1883
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. 2016 · 2016
Earlier work this paper cites.
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation.. In CVPR , Vol. 1. 7
Jose Caballero, Christian Ledig, Andrew P Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. 2017 · 2017
Earlier work this paper cites.
Coherent online video style transfer. In Proc. Intl. Conf. Computer Vision (ICCV)
Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, and Gang Hua. 2017 · 2017
Earlier work this paper cites.
Improved training of wasserstein gans. In Advances in neural information processing systems . 5767–5777
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. 2017 · 2017
Earlier work this paper cites.
Image-To-Image Translation With Conditional Adversarial Networks. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2017 · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017 · 2017
Earlier work this paper cites.
Deep laplacian pyramid networks for fast and accurate superresolution. In IEEE Conference on Computer Vision and Pattern Recognition , Vol. 2. 5
Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. 2017 · 2017
Earlier work this paper cites.
Robust video super-resolution with learned temporal dynamics. In Computer Vision (ICCV), 2017 IEEE International Conference on . IEEE, 2526–2534
Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang. 2017 · 2017
Cited alongside, same era.
Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision . 2794–2802
Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley. 2017 · 2017
Cited alongside, same era.
Enhancenet: Single image super-resolution through automated texture synthesis. In Computer Vision (ICCV), 2017 IEEE International Conference on . IEEE, 4501–4510
Mehdi SM Sajjadi, Bernhard Schölkopf, and Michael Hirsch. 2017 · 2017
Cited alongside, same era.
Detail-Revealing Deep Video Super-Resolution. In The IEEE International Conference on Computer Vision (ICCV)
Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, and Jiaya Jia. 2017 · 2017
Cited alongside, same era.
Perceptual evaluation of liquid simulation methods
End-to-end optimization of optics and image processing for achromatic extended depth of field and super-resolution imaging
Vincent Sitzmann, Steven Diamond, Yifan Peng, Xiong Dun, Stephen Boyd, Wolfgang Heidrich, Felix Heide, and Gordon Wetzstein. 2018 · 2018
Closest in time.
Perceptual adversarial networks for image-to-image transformation
Chaoyue Wang, Chang Xu, Chaohui Wang, and Dacheng Tao. 2018b · 2018
Closest in time.
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
You Xie, Erik Franz, Mengyu Chu, and Nils Thuerey. 2018 · 2018
Closest in time.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018 · 2018
Closest in time.
Large Scale GAN Training for High Fidelity Natural Image Synthesis. In International Conference on Learning Representations
Andrew Brock, Jeff Donahue, and Karen Simonyan. 2019 · 2019
Closest in time.
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Kiwon Um, Xiangyu Hu, and Nils Thuerey. 2017 · 2017
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision . 2223–2232
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017 · 2017
Cited alongside, same era.
Recycle-GAN: Unsupervised Video Retargeting. In The European Conference on Computer Vision (ECCV)
Aayush Bansal, Shugao Ma, Deva Ramanan, and Yaser Sheikh. 2018 · 2018
Cited alongside, same era.
Unsupervised video-to-video translation
Dina Bashkirova, Ben Usman, and Kate Saenko. 2018 · 2018
Cited alongside, same era.
The perception-distortion tradeoff. In Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah, USA . 6228–6237
Yochai Blau and Tomer Michaeli. 2018 · 2018
Cited alongside, same era.
Tears of Steel
(CC) Blender Foundation | | mango.blender.org. 2011 · 2018
Cited alongside, same era.
Coupled fluid density and motion from single views. In Computer Graphics Forum , Vol. 37(8). Wiley Online Library, 47–58
M-L Eckert, Wolfgang Heidrich, and Nils Thuerey. 2018 · 2018
Cited alongside, same era.
Deep Exemplar-Based Colorization
Mingming He, Dongdong Chen, Jing Liao, Pedro V. Sander, and Lu Yuan. 2018 · 2018
Cited alongside, same era.
Yang Chen, Yingwei Pan, Ting Yao, Xinmei Tian, and Tao Mei. 2019 · 2019
Closest in time.
Recurrent back-projection network for video super-resolution. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 3897–3906
Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita. 2019 · 2019
Closest in time.
Stylizing Video by Example
Ondřej Jamriška, Šárka Sochorová, Ondřej Texler, Michal Lukáč, Jakub Fišer, Jingwan Lu, Eli Shechtman, and Daniel Sýkora. 2019 · 2019
Closest in time.
DeepFovea: neural reconstruction for foveated rendering and video compression using learned statistics of natural videos
Anton S Kaplanyan, Anton Sochenov, Thomas Leimkühler, Mikhail Okunev, Todd Goodall, and Gizem Rufo. 2019 · 2019
Closest in time.
Transport-Based Neural Style Transfer for Smoke Simulations
Byungsoo Kim, Vinicius C. Azevedo, Markus Gross, and Barbara Solenthaler. 2019 · 2019
Closest in time.
Selflow: Self-supervised learning of optical flow. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 4571–4580
Pengpeng Liu, Michael Lyu, Irwin King, and Jia Xu. 2019 · 2019
Closest in time.
NTIRE 2019 Challenge on Video Deblurring and Super-Resolution: Dataset and Study. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. 2019 · 2019
Closest in time.
Preserving Semantic and Temporal Consistency for Unpaired Video-to-Video Translation. In Proceedings of the 27th ACM International Conference on Multimedia (Nice, France) (MM ’19) . Association for Computing Machinery, New York, NY, USA, 1248–1257
Kwanyong Park, Sanghyun Woo, Dahun Kim, Donghyeon Cho, and In So Kweon. 2019 · 2019
Closest in time.
Handheld Multi-Frame Super-Resolution
Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. 2019 · 2019
Closest in time.
SAGNet: Structure-aware Generative Network for 3D-Shape Modeling
Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, and Hui Huang. 2019 · 2019
Closest in time.
Deep Exemplar-based Video Colorization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 8052–8061
Bo Zhang, Mingming He, Jing Liao, Pedro V Sander, Lu Yuan, Amine Bermak, and Dong Chen. 2019 · 2019
Closest in time.
Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 9308–9316
Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. 2019 · 2019
Closest in time.