Existing image inpainting methods often produce artifacts
when dealing with large holes in real applications. To address this challenge, we propose an iterative inpainting method with a feedback mechanism. Specifically, we introduce a deep generative model which not only
outputs an inpainting result but also a corresponding confidence map.
Using this map as feedback, it progressively fills the hole by trusting only
high-confidence pixels inside the hole at each iteration and focuses on the
remaining pixels in the next iteration. As it reuses partial predictions
from the previous iterations as known pixels, this process gradually improves the result. In addition, we propose a guided upsampling network
to enable generation of high-resolution inpainting results. We achieve
this by extending the Contextual Attention module [34] to borrow highresolution feature patches in the input image. Furthermore, to mimic
real object removal scenarios, we collect a large object mask dataset and
synthesize more realistic training data that better simulates user inputs.
Experiments show that our method significantly outperforms existing
methods in both quantitative and qualitative evaluations. More results
and Web APP are available at https://zengxianyu.github.io/iic.