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Blind face inpainting refers to the task of reconstructing visual contentswithout explicitly indicating the corrupted regions in a face image.Inherently, this task faces two challenges: (1) how to detect various maskpatterns of different shapes and contents; (2) how to restore visuallyplausible and pleasing contents in the masked regions. In this paper, wepropose a novel two-stage blind face inpainting method named Frequency-guidedTransformer and Top-Down Refinement Network (FT-TDR) to tackle thesechallenges. Specifically, we first use a transformer-based network to detectthe corrupted regions to be inpainted as masks by modeling the relation amongdifferent patches. We also exploit the frequency modality as complementaryinformation for improved detection results and capture the local contextualincoherence to enhance boundary consistency. Then a top-down refinement networkis proposed to hierarchically restore features at different levels and generatecontents that are semantically consistent with the unmasked face regions.Extensive experiments demonstrate that our method outperforms currentstate-of-the-art blind and non-blind face inpainting methods qualitatively andquantitatively.

FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting