Controllable Light Diffusion for Portraits
This addresses the need for better portrait lighting control in photography and computer vision, offering a novel approach compared to existing methods that change entire lighting environments or remove shadows entirely.
The paper tackles the problem of improving lighting in portraits by softening harsh shadows and specular highlights while preserving overall illumination, using a learning-based method that allows controllable light diffusion and generates synthetic external shadows with sub-surface scattering effects, resulting in increased robustness for higher-level vision applications like albedo and geometry estimation.
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.