Regional facial image synthesis conditioned on semantic mask has achieved great success using generative adversarial networks. However, the appearance of different regions may be inconsistent with each other when conducting regional image editing. In this paper, we focus on the problem of harmonized regional style transfer and manipulation for facial images. The proposed approach supports regional style transfer and manipulation at the same time. A multi-scale encoder and style mapping networks are proposed in our work. The encoder is responsible for extracting regional styles of real faces. Style mapping networks generate styles from random samples for all facial regions. As the key part of our work, we propose a multi-region style attention module to adapt the multiple regional style embeddings from a reference image to a target image for generating harmonious and plausible results. Furthermore, we propose a new metric "harmony score" and conduct experiments in a challenging setting: three widely used face datasets are involved and we test the model by transferring the regional facial appearance between datasets. Images in different datasets are usually quite different, which makes the inconsistency between target and reference regions more obvious. Results show that our model can generate reliable style transfer and multi-modal manipulation results compared with SOTAs. Furthermore, we show two face editing applications using the proposed approach.

中文翻译：

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致力于人脸图像的协调区域风格转移和操纵

利用生成式对抗网络，基于语义蒙版的区域人脸图像合成取得了巨大的成功。但是，在进行区域图像编辑时，不同区域的外观可能会彼此不一致。在本文中，我们关注于面部图像的区域风格转移和协调统一的问题。所提出的方法同时支持区域样式的传递和操纵。我们的工作中提出了一种多尺度编码器和样式映射网络。编码器负责提取真实面孔的区域样式。样式映射网络从所有面部区域的随机样本生成样式。作为我们工作的关键部分，我们提出了一种多区域样式关注模块，以将参考图像到目标图像的多个区域样式嵌入进行适配，以生成和谐合理的结果。此外，我们提出了一种新的指标“和谐得分”，并在具有挑战性的环境中进行了实验：涉及三个广泛使用的面部数据集，并且我们通过在数据集之间转移区域性面部外观来测试该模型。不同数据集中的图像通常差异很大，这使得目标区域和参考区域之间的不一致更加明显。结果表明，与SOTA相比，我们的模型可以生成可靠的样式转换和多模式操作结果。此外，我们展示了使用该方法的两个人脸编辑应用程序。这使得目标区域和参考区域之间的不一致更加明显。结果表明，与SOTA相比，我们的模型可以生成可靠的样式转换和多模式操作结果。此外，我们展示了使用该方法的两个人脸编辑应用程序。这使得目标区域和参考区域之间的不一致更加明显。结果表明，与SOTA相比，我们的模型可以生成可靠的样式转换和多模式操作结果。此外，我们展示了使用该方法的两个人脸编辑应用程序。