Controllable Light Diffusion for Portraits

David Futschik Kelvin Ritland James Vecore Sean Fanello Sergio Orts-Escolano Brian Curless Daniel Sýkora Rohit Pandey
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 8412-8421, 2023
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.