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.

翻译：我们引入光扩散技术，一种改善人像光照的新方法，能在保持场景整体照明的同时柔化硬阴影与高光斑。该方法受专业摄影师使用的柔光板及柔光箱启发，仅需单张人像照片即可实现光照柔化。现有的人像重光照方法侧重于改变整个照明环境、去除阴影（忽略强烈高光斑）或完全消除阴影。相比之下，我们提出一种基于学习的方法，能够控制光扩散程度并应用于真实场景人像。此外，我们设计了一种方法，可合成符合人物面部形态的、具有次表面散射效果的合理外部阴影。最后，我们展示该方法如何提升高级视觉任务（如反照率估计、几何估计与语义分割）的稳健性。