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Computer Science > Computer Vision and Pattern Recognition

arXiv:2212.09102 (cs)
[Submitted on 18 Dec 2022 (v1), last revised 27 Sep 2023 (this version, v3)]

Title:Face Generation and Editing with StyleGAN: A Survey

Authors:Andrew Melnik, Maksim Miasayedzenkau, Dzianis Makarovets, Dzianis Pirshtuk, Eren Akbulut, Dennis Holzmann, Tarek Renusch, Gustav Reichert, Helge Ritter
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2212.09102 [cs.CV]
  (or arXiv:2212.09102v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2212.09102
Journal reference: in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 5, pp. 3557-3576, May 2024
Related DOI: https://doi.org/10.1109/TPAMI.2024.3350004

Submission history

From: Andrew Melnik [view email]
[v1] Sun, 18 Dec 2022 15:04:31 UTC (17,385 KB)
[v2] Thu, 13 Apr 2023 11:03:47 UTC (18,962 KB)
[v3] Wed, 27 Sep 2023 14:34:46 UTC (4,752 KB)
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