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arXiv · 2203.12088

Deep Portrait Delighting

Joshua Weir·Junhong Zhao·Andrew Chalmers·Taehyun Rhee
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Abstract

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.

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Joshua Weir, Junhong Zhao, Andrew Chalmers, Taehyun Rhee. 2022-03-22. Deep Portrait Delighting. https://arxiv.org/abs/2203.12088

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