  Face inpainting aims to complete the corrupted regions of the face images,
which requires coordination between the completed areas and the non-corrupted
areas. Recently, memory-oriented methods illustrate great prospects in the
generation related tasks by introducing an external memory module to improve
image coordination. However, such methods still have limitations in restoring
the consistency and continuity for specificfacial semantic parts. In this
paper, we propose the coarse-to-fine Memory-Disentangled Refinement Networks
(MDRNets) for coordinated face inpainting, in which two collaborative modules
are integrated, Disentangled Memory Module (DMM) and Mask-Region Enhanced
Module (MREM). Specifically, the DMM establishes a group of disentangled memory
blocks to store the semantic-decoupled face representations, which could
provide the most relevant information to refine the semantic-level
coordination. The MREM involves a masked correlation mining mechanism to
enhance the feature relationships into the corrupted regions, which could also
make up for the correlation loss caused by memory disentanglement. Furthermore,
to better improve the inter-coordination between the corrupted and
non-corrupted regions and enhance the intra-coordination in corrupted regions,
we design InCo2 Loss, a pair of similarity based losses to constrain the
feature consistency. Eventually, extensive experiments conducted on CelebA-HQ
and FFHQ datasets demonstrate the superiority of our MDRNets compared with
previous State-Of-The-Art methods.
