| 摘要: |
| 自动上妆旨在通过计算机算法实现人脸妆容的编辑与合成，隶属于人脸图像分析领域.其在互动娱乐应用、图像视频编辑、辅助人脸识别等多方面起着重要作用.然而作为人脸编辑任务，其仍难以在保证图像的编辑结果自然、真实的同时又很好地满足编辑需求，并且仍有难以精确控制编辑区域、图像编辑前后一致性差、图像质量不够精细等问题.针对以上难点，创新性地提出了一种掩模控制的自动上妆生成对抗网络，该网络利用掩模方法，能够重点编辑上妆区域，约束人脸妆容编辑中无需编辑的区域不变，保持主体信息.同时其又能单独编辑人脸的眼影、嘴唇、脸颊等局部区域，实现特定区域上妆，丰富了上妆功能.此外，该网络能够进行多数据集联合训练，除妆容数据集外，还可以使用其他人脸数据集作为辅助，增强模型的泛化能力，得到更加自然的上妆结果.最后，依据多种评价标准，进行了充分的定性及定量实验，并与目前的主流算法进行了对比，综合评价了所提方法的性能. |
| 关键词: 生成对抗网络 自动上妆 人脸图像编辑 深度学习 |
| DOI：10.13328/j.cnki.jos.005666 |
| 分类号: |
| 基金项目:国家自然科学基金（U1536203，61572493，61876177） |
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| Automatic Makeup with Region Sensitive Generative Adversarial Networks |
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BAO Ren-Da1, YU Han1, ZHU De-Fa1, HUANG Shao-Fei1, SUN Yao1, LIU Si2
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1.Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China;2.School of Computer Science and Engineering, Beihang University, Beijing 100191, China
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| Abstract: |
| Automatic makeup refers to the editing and synthesis of face makeup through computer algorithms. It belongs to the field of face image analysis, and plays an important role in interactive entertainment applications, image and video editing, and face recognition. However, as a face editing problem, it is still difficult to ensure that the editing result of the image is natural and satisfies the editing requirements. Makeup still has some difficulties such as precisely controlling the editing area is hard, the image consistency before and after editing is poor, and the image quality is insufficient. In response to these difficulties, this study innovatively proposes a mask-controlled automatic makeup generative adversarial network. Through a masking method, this network can edit the makeup area with emphasis, restrict the area that does not require editing, and maintain the key information. At the same time, it can separately edit the eye shadow, lips, cheeks, and other local areas of the face to achieve makeup on specific areas and enrich the makeup function. In addition, this network can be trained jointly on multiple datasets. In addition to makeup dataset, it can also use other face datasets as an aid to enhance the model's generalization ability and get a more natural makeup result. Finally, based on a variety of evaluation methods, more comprehensive qualitative and quantitative experiments are carried out, the results are compared with the other methods, and the performance of the proposed method is comprehensively evaluated. |
| Key words: generative adversarial nets automatic makeup face image editing deep learning |
