宿州学院学报2024，Vol.39Issue(6)：1-7,7.DOI:10.3969/j.issn.1673-2006.2024.06.001
基于生成对抗网络的人脸妆容迁移方法研究
Research on Face Makeup Transfer Method Based on Generative Adversarial Networks
摘要
Abstract
Makeup transfer is a technique in computer vision and deep learning that involves transferring the style of one makeup look to other faces to achieve a high-fidelity makeup effect transformation.To effectively address the issues of makeup transfer,such as makeup region errors and incomplete makeup transfer,a makeup transfer method based on Generative Adversarial Networks(GANs),known as MutNet,is proposed.With the goal of addressing in-complete makeup transfer,this method introduces a spatial attention mechanism in the decoder to help the network focus more on the areas that need modification;incorporating Siamese contrastive loss to better establish semantic correspondences between faces,it effectively mitigates or overcomes makeup region errors.At the same time,the comparative results with other methods show that MutNet can achieve a more coordinated makeup effect.关键词
人脸妆容迁移/人脸图像生成/生成对抗网络/孪生对比损失/空间注意力机制Key words
Face makeup transfer/Face image generation/Generative adversarial networks/Siamese contrastive loss/Spatial attention mechanism分类
信息技术与安全科学引用本文复制引用
孙克雷,潘宇,童波..基于生成对抗网络的人脸妆容迁移方法研究[J].宿州学院学报,2024,39(6):1-7,7.基金项目
国家自然科学基金项目(62076006) （62076006）
安徽省高校重点科研项目(2022AH050821) （2022AH050821）
安徽理工大学研究生创新基金项目(2023cx2126). （2023cx2126）
