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In this paper, we address the problem of degradation in inpainting quality ofneural networks operating at high resolutions. Inpainting networks are oftenunable to generate globally coherent structures at resolutions higher thantheir training set. This is partially attributed to the receptive fieldremaining static, despite an increase in image resolution. Although downscalingthe image prior to inpainting produces coherent structure, it inherently lacksdetail present at higher resolutions. To get the best of both worlds, weoptimize the intermediate featuremaps of a network by minimizing a multiscaleconsistency loss at inference. This runtime optimization improves theinpainting results and establishes a new state-of-the-art for high resolutioninpainting. Code is available at:https://github.com/geomagical/lama-with-refiner/tree/refinement.

Feature Refinement to Improve High Resolution Image Inpainting