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StyleGAN's disentangled style representation enables powerful image editing by manipulating the latent variables, but accurately mapping real-world images to their latent variables (GAN inversion) remains a challenge.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Neural photo editing with introspective adversarial networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, H. Larochelle, and Ole Winther · 2016
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Invertible conditional gans for image editing
Guim Perarnau, Joost Van De Weijer, Bogdan Raducanu, and Jose M Álvarez · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A Efros · 2016
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
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Precise recovery of latent vectors from generative adversarial networks
Zachary Chase Lipton and Subarna Tripathi · 2017
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Precise recovery of latent vectors from generative adversarial networks
Zachary C Lipton and Subarna Tripathi · 2017
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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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Image2stylegan: How to embed images into the stylegan latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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Semantic photo manipulation with a generative image prior
David Bau, Hendrik Strobelt, William S. Peebles, Jonas Wulff, Bolei Zhou, Jun-Yan Zhu, and Antonio Torralba · 2019
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Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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Seeing what a gan cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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Inverting the generator of a generative adversarial network
Antonia Creswell and Anil Anthony Bharath · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Image2stylegan++: How to edit the embedded images?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2020
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Stargan v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Editing in style: Uncovering the local semantics of gans
Edo Collins, Raja Bala, Bob Price, and Sabine Susstrunk · 2020
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Image processing using multi-code gan prior
Jinjin Gu, Yujun Shen, and Bolei Zhou · 2020
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Ganspace: Discovering interpretable gan controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
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Transforming and projecting images into class-conditional generative networks
Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, and Aaron Hertzmann · 2020
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On the ”steerability” of generative adversarial networks
Ali Jahanian, Lucy Chai, and Phillip Isola · 2020
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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.
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.
Exploiting deep generative prior for versatile image restoration and manipulation
Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo · 2020
Cited alongside, same era.
Swapping autoencoder for deep image manipulation
Pivotal tuning for latent-based editing of real images
Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or · 2021
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Designing an encoder for stylegan image manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or · 2021
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Designing an encoder for StyleGAN image manipulation
Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or · 2021
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WarpedGANSpace: Finding non-linear rbf paths in GAN latent space
Christos Tzelepis, Georgios Tzimiropoulos, and Ioannis Patras · 2021
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High-fidelity gan inversion for image attribute editing
Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, and Qifeng Chen · 2021
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Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, and Richard Zhang · 2020
Cited alongside, same era.
Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto · 2020
Cited alongside, same era.
Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto · 2020
Cited alongside, same era.
Interpreting the latent space of GANs for semantic face editing
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou · 2020
Cited alongside, same era.
Interfacegan: Interpreting the disentangled face representation learned by gans
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2020
Cited alongside, same era.
Unsupervised discovery of interpretable directions in the gan latent space
Andrey Voynov and Artem Babenko · 2020
Cited alongside, same era.
In-domain GAN inversion for real image editing
Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou · 2020
Cited alongside, same era.
Tianyi Wei, Dongdong Chen, Wenbo Zhou, Jing Liao, Weiming Zhang, Lu Yuan, Gang Hua, and Nenghai Yu · 2021
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Stylespace analysis: Disentangled controls for stylegan image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2021
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StyleSpace analysis: Disentangled controls for StyleGAN image generation
Zongze Wu, Dani Lischinski, and Eli Shechtman · 2021
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Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
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From continuity to editability: Inverting gans with consecutive images
Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, and Shengfeng He · 2021
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Generative hierarchical features from synthesizing images
Yinghao Xu, Yujun Shen, Jiapeng Zhu, Ceyuan Yang, and Bolei Zhou · 2021
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Dual contrastive loss and attention for gans
Ning Yu, Guilin Liu, Aysegul Dundar, Andrew Tao, Bryan Catanzaro, Larry S Davis, and Mario Fritz · 2021
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Hyperstyle: Stylegan inversion with hypernetworks for real image editing
Yuval Alaluf, Omer Tov, Ron Mokady, Rinon Gal, and Amit Bermano · 2022
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High-fidelity gan inversion with padding space
Qingyan Bai, Yinghao Xu, Jiapeng Zhu, Weihao Xia, Yujiu Yang, and Yujun Shen · 2022
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Enriching stylegan with illumination physics
Anand Bhattad and David A Forsyth · 2022
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Cycle encoding of a stylegan encoder for improved reconstruction and editability
Xudong Mao, Liujuan Cao, Aurele Tohokantche Gnanha, Zhenguo Yang, Qing Li, and Rongrong Ji · 2022
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Interestyle: Encoding an interest region for robust stylegan inversion
Seung Jun Moon and GyeongMoon Park · 2022
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Spatially-adaptive multilayer selection for gan inversion and editing
Gaurav Parmar, Yijun Li, Jingwan Lu, Richard Zhang, Jun-Yan Zhu, and Krishna Kumar Singh · 2022
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Overparameterization improves stylegan inversion
Yohan Poirier-Ginter, Alexandre Lessard, Ryan Smith, and Jean-François Lalonde · 2022
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Editing out-of-domain gan inversion via differential activations
Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, and Shengfeng He · 2022
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Chunkygan: Real image inversion via segments
Adéla Subrtová, David Futschik, Jan Cech, Michal Lukác, Eli Shechtman, and Daniel Sýkora · 2022
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A style-based gan encoder for high fidelity reconstruction of images and videos
Xu Yao, Alasdair Newson, Yann Gousseau, and Pierre Hellier · 2022
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