  Facial image inpainting, with high-fidelity preservation for image realism,
is a very challenging task. This is due to the subtle texture in key facial
features (component) that are not easily transferable. Many image inpainting
techniques have been proposed with outstanding capabilities and high
quantitative performances recorded. However, with facial inpainting, the
features are more conspicuous and the visual quality of the blended inpainted
regions are more important qualitatively. Based on these facts, we design a
foreground-guided facial inpainting framework that can extract and generate
facial features using convolutional neural network layers. It introduces the
use of foreground segmentation masks to preserve the fidelity. Specifically, we
propose a new loss function with semantic capability reasoning of facial
expressions, natural and unnatural features (make-up). We conduct our
experiments using the CelebA-HQ dataset, segmentation masks from CelebAMask-HQ
(for foreground guidance) and Quick Draw Mask (for missing regions). Our
proposed method achieved comparable quantitative results when compare to the
state of the art but qualitatively, it demonstrated high-fidelity preservation
of facial components.
