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.

提出一种基于学习的光扩散方法来改善肖像照片的照明，使得整个场景的光照得以保留，同时柔化过于强烈的阴影和高光，并设计一种方法来生成逼真的外部阴影。最终实现提高高级视觉应用程序如反照率估计、几何估计和语义分割的鲁棒性。