Shadow-Aware Makeup Transfer with Lighting Adaptation

Abstract

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In recent years, makeup transfer methods have shown satisfactory performance on reference images without shadows but struggle with those containing shadows. These methods often mistakenly include shadows as part of the makeup style, leading to poor results. To address this, we propose a shadow-aware makeup transfer approach. This method utilizes facial symmetry by flipping the face features and replacing shadowed areas with shadow-free makeup features. Additionally, we introduce a module to predict shadow maps, guiding makeup transfer from non-shadowed areas in the reference images. We also present a lighting-aware Pseudo Ground Truth (PGT) generator that evaluates shadow presence and lighting quality on the reference face, ensuring high-quality PGT. Experimental results demonstrate our method’s effectiveness in shadow removal and its ability to produce visually pleasing results.