Image inpainting seeks a semantically consistent way to recover the corrupted
image in the light of its unmasked content. Previous approaches usually reuse
the well-trained GAN as effective prior to generate realistic patches for
missing holes with GAN inversion. Nevertheless, the ignorance of a hard
constraint in these algorithms may yield the gap between GAN inversion and
image inpainting. Addressing this problem, in this paper, we devise a novel GAN
inversion model for image inpainting, dubbed InvertFill, mainly consisting of
an encoder with a pre-modulation module and a GAN generator with F&W+ latent
space. Within the encoder, the pre-modulation network leverages multi-scale
structures to encode more discriminative semantics into style vectors. In order
to bridge the gap between GAN inversion and image inpainting, F&W+ latent space
is proposed to eliminate glaring color discrepancy and semantic inconsistency.
To reconstruct faithful and photorealistic images, a simple yet effective
Soft-update Mean Latent module is designed to capture more diverse in-domain
patterns that synthesize high-fidelity textures for large corruptions.
Comprehensive experiments on four challenging datasets, including Places2,
CelebA-HQ, MetFaces, and Scenery, demonstrate that our InvertFill outperforms
the advanced approaches qualitatively and quantitatively and supports the
completion of out-of-domain images well.

本文提出了一种新的用于图像修复的 GAN 反演模型 InvertFill，它包括一个带有预调制模块的编码器和一个包含 F&W + 潜在空间的 GAN 生成器。通过使用 F&W + 潜在空间和 Soft-update Mean Latent 模块，该模型能够更好地实现图像修复。该模型在四个具有挑战性的数据集上的综合实验结果表明，在视觉和定量上优于现有的高级方法，并且很好地支持跨域图像的修复。