We present a deep neural network for removing undesirable shading features from an unconstrained portrait image, recovering the underlying texture. Our training scheme incorporates three regularization strategies: masked loss, to emphasize high-frequency shading features; soft-shadow loss, which improves sensitivity to subtle changes in lighting; and shading-offset estimation, to supervise separation of shading and texture. Our method demonstrates improved delighting quality and generalization when compared with the state-of-the-art. We further demonstrate how our delighting method can enhance the performance of light-sensitive computer vision tasks such as face relighting and semantic parsing, allowing them to handle extreme lighting conditions.
@article{arxiv.2203.12088,
title = {Deep Portrait Delighting},
author = {Joshua Weir and Junhong Zhao and Andrew Chalmers and Taehyun Rhee},
journal= {arXiv preprint arXiv:2203.12088},
year = {2022}
}