Face Generation and Editing with StyleGAN: A Survey
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866910701174390784 |
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| author | Melnik, Andrew Miasayedzenkau, Maksim Makarovets, Dzianis Pirshtuk, Dzianis Akbulut, Eren Holzmann, Dennis Renusch, Tarek Reichert, Gustav Ritter, Helge |
| author_facet | Melnik, Andrew Miasayedzenkau, Maksim Makarovets, Dzianis Pirshtuk, Dzianis Akbulut, Eren Holzmann, Dennis Renusch, Tarek Reichert, Gustav Ritter, Helge |
| contents | Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN. The survey covers the evolution of StyleGAN, from PGGAN to StyleGAN3, and explores relevant topics such as suitable metrics for training, different latent representations, GAN inversion to latent spaces of StyleGAN, face image editing, cross-domain face stylization, face restoration, and even Deepfake applications. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_09102 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Face Generation and Editing with StyleGAN: A Survey Melnik, Andrew Miasayedzenkau, Maksim Makarovets, Dzianis Pirshtuk, Dzianis Akbulut, Eren Holzmann, Dennis Renusch, Tarek Reichert, Gustav Ritter, Helge Computer Vision and Pattern Recognition Machine Learning Our goal with this survey is to provide an overview of the state of the art deep learning methods for face generation and editing using StyleGAN. The survey covers the evolution of StyleGAN, from PGGAN to StyleGAN3, and explores relevant topics such as suitable metrics for training, different latent representations, GAN inversion to latent spaces of StyleGAN, face image editing, cross-domain face stylization, face restoration, and even Deepfake applications. We aim to provide an entry point into the field for readers that have basic knowledge about the field of deep learning and are looking for an accessible introduction and overview. |
| title | Face Generation and Editing with StyleGAN: A Survey |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2212.09102 |