title: Relightify: Relightable 3D Faces from a Single Image via Diffusion
    Models
summary: Following the remarkable success of diffusion models on image generation,
  recent works have also demonstrated their impressive ability to address a
  number of inverse problems in an unsupervised way, by properly constraining the
  sampling process based on a conditioning input. Motivated by this, in this
  paper, we present the first approach to use diffusion models as a prior for
  highly accurate 3D facial BRDF reconstruction from a single image. We start by
  leveraging a high-quality UV dataset of facial reflectance (diffuse and
  specular albedo and normals), which we render under varying illumination
  settings to simulate natural RGB textures and, then, train an unconditional
  diffusion model on concatenated pairs of rendered textures and reflectance
  components. At test time, we fit a 3D morphable model to the given image and
  unwrap the face in a partial UV texture. By sampling from the diffusion model,
  while retaining the observed texture part intact, the model inpaints not only
  the self-occluded areas but also the unknown reflectance components, in a
  single sequence of denoising steps. In contrast to existing methods, we
  directly acquire the observed texture from the input image, thus, resulting in
  more faithful and consistent reflectance estimation. Through a series of
  qualitative and quantitative comparisons, we demonstrate superior performance
  in both texture completion as well as reflectance reconstruction tasks.
paper_authors._id: 645c3c89fbdc26a155fc80d4
paper_authors.name: Foivos Paraperas Papantoniou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-11T14:59:56.390Z
paper_authors._id: 645d00f703fc86c46b3e57eb
paper_authors.avatarUrl: /avatars/1240d1915f051c18f31e342a423c9c24.svg
paper_authors.fullname: Foivos Paraperas
paper_authors.type: user
paper_authors.user: FoivosPar
paper_authors._id: 645c3c89fbdc26a155fc80d5
paper_authors.name: Alexandros Lattas
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-12T08:10:58.459Z
paper_authors._id: 645df1be8a22774edb290765
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/OduGNNzXnhkwnPkBZUw3B.jpeg
paper_authors.fullname: Alexandros Lattas
paper_authors.type: user
paper_authors.user: lattas
paper_authors._id: 645c3c89fbdc26a155fc80d6
paper_authors.name: Stylianos Moschoglou
paper_authors._id: 645c3c89fbdc26a155fc80d7
paper_authors.name: Stefanos Zafeiriou
paper_title: Relightify: Relightable 3D Faces from a Single Image via Diffusion
    Models
paper_summary: Following the remarkable success of diffusion models on image generation,
  recent works have also demonstrated their impressive ability to address a
  number of inverse problems in an unsupervised way, by properly constraining the
  sampling process based on a conditioning input. Motivated by this, in this
  paper, we present the first approach to use diffusion models as a prior for
  highly accurate 3D facial BRDF reconstruction from a single image. We start by
  leveraging a high-quality UV dataset of facial reflectance (diffuse and
  specular albedo and normals), which we render under varying illumination
  settings to simulate natural RGB textures and, then, train an unconditional
  diffusion model on concatenated pairs of rendered textures and reflectance
  components. At test time, we fit a 3D morphable model to the given image and
  unwrap the face in a partial UV texture. By sampling from the diffusion model,
  while retaining the observed texture part intact, the model inpaints not only
  the self-occluded areas but also the unknown reflectance components, in a
  single sequence of denoising steps. In contrast to existing methods, we
  directly acquire the observed texture from the input image, thus, resulting in
  more faithful and consistent reflectance estimation. Through a series of
  qualitative and quantitative comparisons, we demonstrate superior performance
  in both texture completion as well as reflectance reconstruction tasks.
paper_ai_keywords: diffusion models image generation inverse problems conditioning input 3D facial BRDF reconstruction UV dataset facial reflectance diffuse albedo specular albedo normals illumination settings RGB textures unconditional diffusion model rendered textures reflectance components 3D morphable model UV texture denoising steps texture inpainting reflectance reconstruction self-occluded areas gold descriptions texture completion