  Blind face inpainting refers to the task of reconstructing visual contents
without explicitly indicating the corrupted regions in a face image.
Inherently, this task faces two challenges: (1) how to detect various mask
patterns of different shapes and contents; (2) how to restore visually
plausible and pleasing contents in the masked regions. In this paper, we
propose a novel two-stage blind face inpainting method named Frequency-guided
Transformer and Top-Down Refinement Network (FT-TDR) to tackle these
challenges. Specifically, we first use a transformer-based network to detect
the corrupted regions to be inpainted as masks by modeling the relation among
different patches. We also exploit the frequency modality as complementary
information for improved detection results and capture the local contextual
incoherence to enhance boundary consistency. Then a top-down refinement network
is proposed to hierarchically restore features at different levels and generate
contents that are semantically consistent with the unmasked face regions.
Extensive experiments demonstrate that our method outperforms current
state-of-the-art blind and non-blind face inpainting methods qualitatively and
quantitatively.
