The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN
  Inversion and High Quality Image Editing
The task of manipulating real image attributes through StyleGAN inversion has
been extensively researched. This process involves searching latent variables
from a well-trained StyleGAN generator that can synthesize a real image,
modifying these latent variables, and then synthesizing an image with the
desired edits. A balance must be struck between the quality of the
reconstruction and the ability to edit. Earlier studies utilized the
low-dimensional W-space for latent search, which facilitated effective editing
but struggled with reconstructing intricate details. More recent research has
turned to the high-dimensional feature space F, which successfully inverses the
input image but loses much of the detail during editing. In this paper, we
introduce StyleFeatureEditor -- a novel method that enables editing in both
w-latents and F-latents. This technique not only allows for the reconstruction
of finer image details but also ensures their preservation during editing. We
also present a new training pipeline specifically designed to train our model
to accurately edit F-latents. Our method is compared with state-of-the-art
encoding approaches, demonstrating that our model excels in terms of
reconstruction quality and is capable of editing even challenging out-of-domain
examples. Code is available at
https://github.com/AIRI-Institute/StyleFeatureEditor.
66718953bb244a42b6ebc340
Denis Bobkov
66718953bb244a42b6ebc341
Vadim Titov
claimed_verified
2024-09-06T09:06:15.712Z
66a8085f306528869adf91a9
https://cdn-avatars.huggingface.co/v1/production/uploads/66a8085f306528869adf91a9/UpnEthxKTfWKkSiMTWPpT.jpeg
Vadim Titov
user
macderru
66718953bb244a42b6ebc342
Aibek Alanov
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2024-06-20T10:19:29.679Z
66680c6451545a8b46c6fd21
/avatars/03707f5ea4e2aa8dc825a9782b00ed85.svg
Aibek Alanov
user
ai-alanov
66718953bb244a42b6ebc343
Dmitry Vetrov
The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN
  Inversion and High Quality Image Editing
The task of manipulating real image attributes through StyleGAN inversion has
been extensively researched. This process involves searching latent variables
from a well-trained StyleGAN generator that can synthesize a real image,
modifying these latent variables, and then synthesizing an image with the
desired edits. A balance must be struck between the quality of the
reconstruction and the ability to edit. Earlier studies utilized the
low-dimensional W-space for latent search, which facilitated effective editing
but struggled with reconstructing intricate details. More recent research has
turned to the high-dimensional feature space F, which successfully inverses the
input image but loses much of the detail during editing. In this paper, we
introduce StyleFeatureEditor -- a novel method that enables editing in both
w-latents and F-latents. This technique not only allows for the reconstruction
of finer image details but also ensures their preservation during editing. We
also present a new training pipeline specifically designed to train our model
to accurately edit F-latents. Our method is compared with state-of-the-art
encoding approaches, demonstrating that our model excels in terms of
reconstruction quality and is capable of editing even challenging out-of-domain
examples. Code is available at
https://github.com/AIRI-Institute/StyleFeatureEditor.
StyleFeatureEditor combines W-space and F-space for effective image attribute manipulation in StyleGAN, balancing reconstruction quality and detailed editing.
StyleGAN inversion latent variables W-space F-space StyleFeatureEditor reconstruction image editing F-latents training pipeline