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• W4313002164 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." @default.
• W4313002164 created "2023-01-05" @default.
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• W4313002164 date "2022-01-01" @default.
• W4313002164 modified "2023-09-25" @default.
• W4313002164 title "Deep Portrait Delighting" @default.
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• W4313002164 doi "https://doi.org/10.1007/978-3-031-19787-1_24" @default.
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