title: The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN
    Inversion and High Quality Image Editing
summary: 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.
paper_authors._id: 66718953bb244a42b6ebc340
paper_authors.name: Denis Bobkov
paper_authors._id: 66718953bb244a42b6ebc341
paper_authors.name: Vadim Titov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-06T09:06:15.712Z
paper_authors._id: 66a8085f306528869adf91a9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66a8085f306528869adf91a9/UpnEthxKTfWKkSiMTWPpT.jpeg
paper_authors.fullname: Vadim Titov
paper_authors.type: user
paper_authors.user: macderru
paper_authors._id: 66718953bb244a42b6ebc342
paper_authors.name: Aibek Alanov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-20T10:19:29.679Z
paper_authors._id: 66680c6451545a8b46c6fd21
paper_authors.avatarUrl: /avatars/03707f5ea4e2aa8dc825a9782b00ed85.svg
paper_authors.fullname: Aibek Alanov
paper_authors.type: user
paper_authors.user: ai-alanov
paper_authors._id: 66718953bb244a42b6ebc343
paper_authors.name: Dmitry Vetrov
paper_title: The Devil is in the Details: StyleFeatureEditor for Detail-Rich StyleGAN
    Inversion and High Quality Image Editing
paper_summary: 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.
paper_ai_keywords: StyleGAN latent variables synthesis low-dimensional W-space high-dimensional feature space F StyleFeatureEditor edits resolution image details inversion F-latents perplexity training pipeline reconstruction quality out-of-domain state-of-the-art encoding approaches