Li Qiming, Tu Tongyue
Department of Computer Science and Technology, Shanghai Maritime University, Shanghai 201306, China.
Math Biosci Eng. 2023 Jan;20(1):737-757. doi: 10.3934/mbe.2023034. Epub 2022 Oct 14.
Facial makeup transfer is a special form of image style transfer. For the reference makeup image with large-pose, improving the quality of the image generated after makeup transfer is still a challenging problem worthy of discussion. In this paper, a large-pose makeup transfer algorithm based on generative adversarial network (GAN) is proposed. First, a face alignment module (FAM) is introduced to locate the key points, such as the eyes, mouth and skin. Secondly, a face parsing module (FPM) and face parsing losses are designed to analyze the source image and extract the face features. Then, the makeup style code is extracted from the reference image and the makeup transfer is completed through integrating facial features and makeup style code. Finally, a large-pose makeup transfer (LPMT) dataset is collected and constructed. Experiments are carried out on the traditional makeup transfer (MT) dataset and the new LPMT dataset. The results show that the image quality generated by the proposed method is better than that of the latest method for large-pose makeup transfer.
面部妆容迁移是图像风格迁移的一种特殊形式。对于大姿态的参考妆容图像，提高妆容迁移后生成图像的质量仍然是一个值得探讨的具有挑战性的问题。本文提出了一种基于生成对抗网络（GAN）的大姿态妆容迁移算法。首先，引入面部对齐模块（FAM）来定位眼睛、嘴巴和皮肤等关键点。其次，设计面部解析模块（FPM）和面部解析损失来分析源图像并提取面部特征。然后，从参考图像中提取妆容风格代码，并通过整合面部特征和妆容风格代码完成妆容迁移。最后，收集并构建了一个大姿态妆容迁移（LPMT）数据集。在传统妆容迁移（MT）数据集和新的LPMT数据集上进行了实验。结果表明，所提方法生成的图像质量优于最新的大姿态妆容迁移方法。
