Makeup transfer is a process of transferring the makeup style from a reference image to the source images, while preserving the source images' identities. This technique is highly desirable and finds many applications. However, existing methods lack fine-level control of the makeup style, making it challenging to achieve high-quality results when dealing with large spatial misalignments. To address this problem, we propose a novel Spatial Alignment and Region-Adaptive normalization method (SARA) in this paper. Our method generates detailed makeup transfer results that can handle large spatial misalignments and achieve part-specific and shade-controllable makeup transfer. Specifically, SARA comprises three modules: Firstly, a spatial alignment module that preserves the spatial context of makeup and provides a target semantic map for guiding the shape-independent style codes. Secondly, a region-adaptive normalization module that decouples shape and makeup style using per-region encoding and normalization, which facilitates the elimination of spatial misalignments. Lastly, a makeup fusion module blends identity features and makeup style by injecting learned scale and bias parameters. Experimental results show that our SARA method outperforms existing methods and achieves state-of-the-art performance on two public datasets.

翻译：妆容迁移是将参考图像的妆容风格迁移至源图像，同时保留源图像身份特征的过程。该技术具有广泛需求和应用前景。然而现有方法缺乏对妆容风格的精细控制，在处理空间错位较大的图像时难以获得高质量结果。针对这一问题，本文提出一种新颖的空间对齐与区域自适应归一化方法（SARA）。该方法可生成包含精细细节的妆容迁移结果，能够处理大规模空间错位，并实现部位特定与色调可控的妆容迁移。具体而言，SARA包含三个模块：首先，空间对齐模块保留妆容的空间上下文信息，并为独立于形状的风格编码提供目标语义图；其次，区域自适应归一化模块通过逐区域编码与归一化解耦妆容形状与风格，有效消除空间错位；最后，妆容融合模块通过注入学习得到的缩放因子与偏置参数，将身份特征与妆容风格进行融合。实验结果表明，本文提出的SARA方法在两个公开数据集上均优于现有方法，达到了当前最优性能水平。