  Over the last few years, deep learning techniques have yielded significant
improvements in image inpainting. However, many of these techniques fail to
reconstruct reasonable structures as they are commonly over-smoothed and/or
blurry. This paper develops a new approach for image inpainting that does a
better job of reproducing filled regions exhibiting fine details. We propose a
two-stage adversarial model EdgeConnect that comprises of an edge generator
followed by an image completion network. The edge generator hallucinates edges
of the missing region (both regular and irregular) of the image, and the image
completion network fills in the missing regions using hallucinated edges as a
priori. We evaluate our model end-to-end over the publicly available datasets
CelebA, Places2, and Paris StreetView, and show that it outperforms current
state-of-the-art techniques quantitatively and qualitatively. Code and models
available at: https://github.com/knazeri/edge-connect
