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 illumi-nation. Inspired by professional photographers' diffusers and scrims, our method softens lighting given only a single portrait pho...

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Bibliographic Details
Published inProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 8412 - 8421
Main Authors Futschik, David, Ritland, Kelvin, Vecore, James, Fanello, Sean, Orts-Escolano, Sergio, Curless, Brian, Sykora, Daniel, Pandey, Rohit
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2023
Subjects
body
Estimation
Face recognition
Geometry
gesture
Humans: Face
Learning systems
Lighting
movement
pose
Semantic segmentation
Shape
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Summary:We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumi-nation. 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.
ISSN:1063-6919
DOI:10.1109/CVPR52729.2023.00813