  Although achieving significant progress, existing deep generative inpainting
methods are far from real-world applications due to the low generalization
across different scenes. As a result, the generated images usually contain
artifacts or the filled pixels differ greatly from the ground truth.
Image-level predictive filtering is a widely used image restoration technique,
predicting suitable kernels adaptively according to different input scenes.
Inspired by this inherent advantage, we explore the possibility of addressing
image inpainting as a filtering task. To this end, we first study the
advantages and challenges of image-level predictive filtering for image
inpainting: the method can preserve local structures and avoid artifacts but
fails to fill large missing areas. Then, we propose semantic filtering by
conducting filtering on the deep feature level, which fills the missing
semantic information but fails to recover the details. To address the issues
while adopting the respective advantages, we propose a novel filtering
technique, i.e., Multilevel Interactive Siamese Filtering (MISF), which
contains two branches: kernel prediction branch (KPB) and semantic & image
filtering branch (SIFB). These two branches are interactively linked: SIFB
provides multi-level features for KPB while KPB predicts dynamic kernels for
SIFB. As a result, the final method takes the advantage of effective semantic &
image-level filling for high-fidelity inpainting. We validate our method on
three challenging datasets, i.e., Dunhuang, Places2, and CelebA. Our method
outperforms state-of-the-art baselines on four metrics, i.e., L1, PSNR, SSIM,
and LPIPS. Please try the released code and model at
https://github.com/tsingqguo/misf.
