In recent years, with the rapid development of adversarial learning technology, facial attribute editing has made great success in a number of areas. Realistic visual effect, invariant identity information, and accurate editing area are the three key issues of facial attribute editing. Unfortunately, most researches focus on the former two problems. However, lack of awareness of the accurate editing area in the task is the main reason for damaging attribute-irrelevant details. To address this issue, this article proposes a novel facial attribute editing algorithm—a generative adversarial network (GAN) with semantic masks—from the perspective of editing location accuracy. By generating the mask with respect to attribute-related areas, the semantic segmentation network can only constrain the manipulation in the target region while not harming any attribute-irrelevant details. The GAN is then combined with the semantic segmentation network to formulate the entire framework, which is referred to as SM-GAN. Extensive experiments on the public datasets CelebA and LFWA prove that the presented method can not only ensure that the attribute manipulation is realistic, but also allow attribute-irrelevant regions to remain unchanged. Moreover, it can also simultaneously edit multiple facial attributes.

中文翻译：

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利用语义分割掩码进行准确的面部属性编辑

近年来，随着对抗性学习技术的飞速发展，人脸属性编辑在多个领域取得了巨大成功。逼真的视觉效果、不变的身份信息和准确的编辑区域是人脸属性编辑的三个关键问题。不幸的是，大多数研究都集中在前两个问题上。然而，缺乏对任务中准确编辑区域的认识是破坏属性无关细节的主要原因。为了解决这个问题，本文从编辑位置准确性的角度提出了一种新颖的面部属性编辑算法——带有语义掩码的生成对抗网络（GAN）。通过生成与属性相关的区域的掩码，语义分割网络只能限制目标区域的操作，而不会损害任何与属性无关的细节。然后将 GAN 与语义分割网络相结合，形成整个框架，称为 SM-GAN。在公共数据集 CelebA 和 LFWA 上进行的大量实验证明，所提出的方法不仅可以确保属性操作是现实的，而且可以让属性无关区域保持不变。此外，它还可以同时编辑多个面部属性。在公共数据集 CelebA 和 LFWA 上进行的大量实验证明，所提出的方法不仅可以确保属性操作是现实的，而且可以让属性无关区域保持不变。此外，它还可以同时编辑多个面部属性。在公共数据集 CelebA 和 LFWA 上进行的大量实验证明，所提出的方法不仅可以确保属性操作是现实的，而且可以让属性无关区域保持不变。此外，它还可以同时编辑多个面部属性。