Facial attribute editing has gained increasing attention recently. Previous methods tackle this challenge by incorporating encoder–decoder and generative adversarial networks. However, the bottleneck layer in encoder–decoder of these methods often leads to blurry and low-quality editing results. And skip connections are used between deep and shallow layers to improve image quality but suffer from a limited ability to manipulate attribute. To address these issues, we propose a novel Facial Attribute Editing Generative Adversarial Networks from a selective refinement perspective, which is capable of focusing on editing the image attributes to be changed while preserving its unique details. Specifically, our method first learns a spatially varying function that maps a high-level feature map to an appropriate parameter map of the normalization layer. Then, by utilizing the residual block, the low-level feature map is added to the feature map after modulation, making the attribute refinement task easier. Experimental results show the superiority of our method in both performances of the attribute manipulation accuracy and perception quality.

面部属性编辑最近受到越来越多的关注。以前的方法通过结合编码器-解码器和生成对抗网络来解决这一挑战。然而，这些方法的编码器-解码器中的瓶颈层通常会导致编辑结果模糊和低质量。并且在深层和浅层之间使用跳过连接来提高图像质量，但操作属性的能力有限。为了解决这些问题，我们从选择性细化的角度提出了一种新颖的面部属性编辑生成对抗网络，它能够专注于编辑要更改的图像属性，同时保留其独特的细节。具体来说，我们的方法首先学习一个空间变化函数，该函数将高级特征图映射到归一化层的适当参数图。然后，利用残差块，将低级特征图添加到调制后的特征图中，使属性细化任务更容易。实验结果表明，我们的方法在属性操纵精度和感知质量方面均具有优越性。