Learning Position-Aware Implicit Neural Network for Real-World Face
  Inpainting
Bo Zhao Huan Yang Jianlong Fu
Face inpainting requires the model to have a precise global understanding of
the facial position structure. Benefiting from the powerful capabilities of
deep learning backbones, recent works in face inpainting have achieved decent
performance in ideal setting (square shape with $512px$). However, existing
methods often produce a visually unpleasant result, especially in the
position-sensitive details (e.g., eyes and nose), when directly applied to
arbitrary-shaped images in real-world scenarios. The visually unpleasant
position-sensitive details indicate the shortcomings of existing methods in
terms of position information processing capability. In this paper, we propose
an \textbf{I}mplicit \textbf{N}eural \textbf{I}npainting \textbf{N}etwork
(IN$^2$) to handle arbitrary-shape face images in real-world scenarios by
explicit modeling for position information. Specifically, a downsample
processing encoder is proposed to reduce information loss while obtaining the
global semantic feature. A neighbor hybrid attention block is proposed with a
hybrid attention mechanism to improve the facial understanding ability of the
model without restricting the shape of the input. Finally, an implicit neural
pyramid decoder is introduced to explicitly model position information and
bridge the gap between low-resolution features and high-resolution output.
Extensive experiments demonstrate the superiority of the proposed method in
real-world face inpainting task.