title: Controllable Light Diffusion for Portraits
summary: We introduce light diffusion, a novel method to improve lighting in
  portraits, softening harsh shadows and specular highlights while preserving
  overall scene illumination. Inspired by professional photographers' diffusers
  and scrims, our method softens lighting given only a single portrait photo.
  Previous portrait relighting approaches focus on changing the entire lighting
  environment, removing shadows (ignoring strong specular highlights), or
  removing shading entirely. In contrast, we propose a learning based method that
  allows us to control the amount of light diffusion and apply it on in-the-wild
  portraits. Additionally, we design a method to synthetically generate plausible
  external shadows with sub-surface scattering effects while conforming to the
  shape of the subject's face. Finally, we show how our approach can increase the
  robustness of higher level vision applications, such as albedo estimation,
  geometry estimation and semantic segmentation.
paper_authors._id: 64599d874fe72fae522b2d77
paper_authors.name: David Futschik
paper_authors._id: 64599d874fe72fae522b2d78
paper_authors.name: Kelvin Ritland
paper_authors._id: 64599d874fe72fae522b2d79
paper_authors.name: James Vecore
paper_authors._id: 64599d874fe72fae522b2d7a
paper_authors.name: Sean Fanello
paper_authors._id: 64599d874fe72fae522b2d7b
paper_authors.name: Sergio Orts-Escolano
paper_authors._id: 64599d874fe72fae522b2d7c
paper_authors.name: Brian Curless
paper_authors._id: 64599d874fe72fae522b2d7d
paper_authors.name: Daniel Sýkora
paper_authors._id: 64599d874fe72fae522b2d7e
paper_authors.name: Rohit Pandey
paper_title: Controllable Light Diffusion for Portraits
paper_summary: We introduce light diffusion, a novel method to improve lighting in
  portraits, softening harsh shadows and specular highlights while preserving
  overall scene illumination. Inspired by professional photographers' diffusers
  and scrims, our method softens lighting given only a single portrait photo.
  Previous portrait relighting approaches focus on changing the entire lighting
  environment, removing shadows (ignoring strong specular highlights), or
  removing shading entirely. In contrast, we propose a learning based method that
  allows us to control the amount of light diffusion and apply it on in-the-wild
  portraits. Additionally, we design a method to synthetically generate plausible
  external shadows with sub-surface scattering effects while conforming to the
  shape of the subject's face. Finally, we show how our approach can increase the
  robustness of higher level vision applications, such as albedo estimation,
  geometry estimation and semantic segmentation.
paper_ai_summary: A novel learning-based method improves lighting in portraits by softening shadows and specular highlights while preserving overall illumination, and enhances higher-level vision applications.
paper_ai_keywords: light diffusion hashing specular highlights external shadows sub-surface scattering albedo estimation geometry estimation semantic segmentation