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The degree of difficulty in image inpainting depends on the types and sizesof the missing parts. Existing image inpainting approaches usually encounterdifficulties in completing the missing parts in the wild with pleasing visualand contextual results as they are trained for either dealing with one specifictype of missing patterns (mask) or unilaterally assuming the shapes and/orsizes of the masked areas. We propose a deep generative inpainting network,named DeepGIN, to handle various types of masked images. We design a SpatialPyramid Dilation (SPD) ResNet block to enable the use of distant features forreconstruction. We also employ Multi-Scale Self-Attention (MSSA) mechanism andBack Projection (BP) technique to enhance our inpainting results. Our DeepGINoutperforms the state-of-the-art approaches generally, including two publiclyavailable datasets (FFHQ and Oxford Buildings), both quantitatively andqualitatively. We also demonstrate that our model is capable of completingmasked images in the wild.

DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting