  Portrait images often suffer from undesirable shadows cast by casual objects
or even the face itself. While existing methods for portrait shadow removal
require training on a large-scale synthetic dataset, we propose the first
unsupervised method for portrait shadow removal without any training data. Our
key idea is to leverage the generative facial priors embedded in the
off-the-shelf pretrained StyleGAN2. To achieve this, we formulate the shadow
removal task as a layer decomposition problem: a shadowed portrait image is
constructed by the blending of a shadow image and a shadow-free image. We
propose an effective progressive optimization algorithm to learn the
decomposition process. Our approach can also be extended to portrait tattoo
removal and watermark removal. Qualitative and quantitative experiments on a
real-world portrait shadow dataset demonstrate that our approach achieves
comparable performance with supervised shadow removal methods. Our source code
is available at
https://github.com/YingqingHe/Shadow-Removal-via-Generative-Priors.
