We present EXE-GAN, a novel exemplar-guided facial inpainting framework using
generative adversarial networks. Our approach can not only preserve the quality
of the input facial image but also complete the image with exemplar-like facial
attributes. We achieve this by simultaneously leveraging the global style of
the input image, the stochastic style generated from the random latent code,
and the exemplar style of exemplar image. We introduce a novel attribute
similarity metric to encourage networks to learn the style of facial attributes
from the exemplar in a self-supervised way. To guarantee the natural transition
across the boundaries of inpainted regions, we introduce a novel spatial
variant gradient backpropagation technique to adjust the loss gradients based
on the spatial location. Extensive evaluations and practical applications on
public CelebA-HQ and FFHQ datasets validate the superiority of EXE-GAN in terms
of the visual quality in facial inpainting.

我们提出了一种新颖的基于示例引导的面部修复框架 EXE-GAN，它利用生成对抗网络来维护原始面部图像的质量，并通过同步使用输入图像的全局风格，随机潜在编码产生的随机风格和范例图像的示例风格来完整图像的典型面部属性。为了保证修复区域的自然过渡，我们介绍了一种新颖的空间变量梯度反向传播技术，通过基于空间位置调整损失梯度来实现。在公共 CelebA-HQ 和 FFHQ 数据集上的广泛评估和实际应用验证了 EXE-GAN 在面部修复的视觉质量方面的优越性。