High-Fidelity Image Inpainting with GAN Inversion

Yongsheng Yu; Libo Zhang; Heng Fan; Tiejian Luo

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arXiv · 2208.11850

High-Fidelity Image Inpainting with GAN Inversion

Yongsheng Yu·Libo Zhang·Heng Fan·Tiejian Luo

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Abstract

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.

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BibTeX RIS

Yongsheng Yu, Libo Zhang, Heng Fan, Tiejian Luo. 2022-08-25. High-Fidelity Image Inpainting with GAN Inversion. https://arxiv.org/abs/2208.11850

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