Personalized facial makeup transfer based on outline correspondence - Gao, Mengying; Wang, Pengjie (Computer animation and virtual worlds, 2024)
Most existing makeup transfer techniques focus on light makeup styles, which limits the task of makeup transfer to color manipulation issues such as eye shadow and lip gloss. However, the makeup in real life is diverse and personalized, not only the most basic foundation, eye makeup, but also the painted patterns on the face, jewelry decoration and other personalized makeup. Inspired by the painting steps of drawing the outline first and then coloring, we propose a makeup transfer network for personalized makeup, which realizes face makeup transfer by learning outline correspondence. Specifically, we propose the outline feature extraction module and outline loss that can promote outline correspondence. Our network can not only transfer daily light makeup, but also complete transfer for complex facial painting patterns. Experiments show that our method can obtain visually more accurate makeup transfer results. Quantitative and qualitative experimental results show that the method proposed in this paper achieves superior results in extreme makeup transfer compared to the state‐of‐the‐art methods. In this paper, we propose a novel makeup transfer method, which first extracts the outline features of the images, and then establishes the outline correspondence between the two images through the self‐attention mechanism. Our method successfully solves the problem of incomplete transfer of colored patterns in the task of extreme makeup transfer. Experiments demonstrate that the method can not only transfer extreme makeup, but also transfer daily light makeup well.