  Existing face relighting methods often struggle with two problems:
maintaining the local facial details of the subject and accurately removing and
synthesizing shadows in the relit image, especially hard shadows. We propose a
novel deep face relighting method that addresses both problems. Our method
learns to predict the ratio (quotient) image between a source image and the
target image with the desired lighting, allowing us to relight the image while
maintaining the local facial details. During training, our model also learns to
accurately modify shadows by using estimated shadow masks to emphasize on the
high-contrast shadow borders. Furthermore, we introduce a method to use the
shadow mask to estimate the ambient light intensity in an image, and are thus
able to leverage multiple datasets during training with different global
lighting intensities. With quantitative and qualitative evaluations on the
Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully
maintains the local facial details of the subject and can accurately handle
hard shadows while achieving state-of-the-art face relighting performance.
