Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
This work addresses facial image editing for computer vision applications, presenting an incremental improvement with a novel method for a known bottleneck.
The paper tackles facial image inpainting by introducing EXE-GAN, a framework that preserves input quality and completes images with exemplar-like attributes, achieving superior visual quality on CelebA-HQ and FFHQ datasets.
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.