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Language guided image inpainting aims to fill in the defective regions of animage under the guidance of text while keeping non-defective regions unchanged.However, the encoding process of existing models suffers from either receptivespreading of defective regions or information loss of non-defective regions,giving rise to visually unappealing inpainting results. To address the aboveissues, this paper proposes N\"UWA-LIP by incorporating defect-free VQGAN(DF-VQGAN) with multi-perspective sequence to sequence (MP-S2S). In particular,DF-VQGAN introduces relative estimation to control receptive spreading andadopts symmetrical connections to protect information. MP-S2S further enhancesvisual information from complementary perspectives, including both low-levelpixels and high-level tokens. Experiments show that DF-VQGAN performs morerobustness than VQGAN. To evaluate the inpainting performance of our model, webuilt up 3 open-domain benchmarks, where N\"UWA-LIP is also superior to recentstrong baselines.

N\"UWA-LIP: Language Guided Image Inpainting with Defect-free VQGAN