  Face inpainting aims at plausibly predicting missing pixels of face images
within a corrupted region. Most existing methods rely on generative models
learning a face image distribution from a big dataset, which produces
uncontrollable results, especially with large-scale missing regions. To
introduce strong control for face inpainting, we propose a novel
reference-guided face inpainting method that fills the large-scale missing
region with identity and texture control guided by a reference face image.
However, generating high-quality results under imposing two control signals is
challenging. To tackle such difficulty, we propose a dual control one-stage
framework that decouples the reference image into two levels for flexible
control: High-level identity information and low-level texture information,
where the identity information figures out the shape of the face and the
texture information depicts the component-aware texture. To synthesize
high-quality results, we design two novel modules referred to as Half-AdaIN and
Component-Wise Style Injector (CWSI) to inject the two kinds of control
information into the inpainting processing. Our method produces realistic
results with identity and texture control faithful to reference images. To the
best of our knowledge, it is the first work to concurrently apply identity and
component-level controls in face inpainting to promise more precise and
controllable results. Code is available at
https://github.com/WuyangLuo/RefFaceInpainting
