Controllable Light Diffusion for Portraits
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.
64599d874fe72fae522b2d77
David Futschik
64599d874fe72fae522b2d78
Kelvin Ritland
64599d874fe72fae522b2d79
James Vecore
64599d874fe72fae522b2d7a
Sean Fanello
64599d874fe72fae522b2d7b
Sergio Orts-Escolano
64599d874fe72fae522b2d7c
Brian Curless
64599d874fe72fae522b2d7d
Daniel Sýkora
64599d874fe72fae522b2d7e
Rohit Pandey
Controllable Light Diffusion for Portraits
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.
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.
light diffusion hashing specular highlights external shadows sub-surface scattering albedo estimation geometry estimation semantic segmentation