In-N-Out: Face Video Inversion and Editing with Volumetric Decomposition

Xu, Yang; Shu, Zhixin; Smith, Cameron M.; Huang, Jia-Bin; Oh, Seoung Wug

doi:10.48550/arxiv.2302.04871

Cited by 2 publications

(2 citation statements)

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“…• Neural Rendering: The 3D consistency of a generated face when viewed from different angles is another crucial aspect of face perception. NeRF [109] is a technique that has shown impressive results for 3D scenes with faces [137], and there have been efforts to combine StyleGAN and NeRF [110] or to create 3D-GANs [138] [139] [140] [141] and solve inversion task for them [142] [143].…”

Section: Discussionmentioning

confidence: 99%

Face Generation and Editing With StyleGAN: A Survey

Melnik,

Miasayedzenkau,

Makaravets

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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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. Awesome list: https://github.com/ndrwmlnk/awesome-face-generation-and-editing

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Section: Discussionmentioning

confidence: 99%

Face Generation and Editing With StyleGAN: A Survey

Melnik,

Miasayedzenkau,

Makaravets

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

View full text Add to dashboard Cite

show abstract

“…3D GAN inversion approaches [49,54,70,79,89] follow the concept of 2D GAN inversion but (jointly) optimize the face latent code and part of the camera parameters to achieve 3D consistent reconstruction and manipulation. Some recent methods [54] utilize the pre-trained EG3D [19] or retrain the model [70] to manipulate a single portrait image.…”

Section: Gan Inversion Gan Inversion Maps a Real Image Backmentioning

confidence: 99%