Resolution-robust Large Mask Inpainting with Fourier Convolutions

WACV 2022

LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than it saw during training (256x256), and achieves the excellent performance even in challenging scenarios, e.g. completion of periodic structures.


Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. We find that one of the main reasons for that is the lack of an ef-fective receptive field in both the inpainting network andthe loss function. To alleviate this issue, we propose anew method called large mask inpainting (LaMa). LaM ais based on:
  • a new inpainting network architecture that uses fast Fourier convolutions, which have the image-widereceptive field
  • a high receptive field perceptual loss;
  • large training masks, which unlocks the potential ofthe first two components.
Our inpainting network improves the state-of-the-art across a range of datasets and achieves excellent performance even in challenging scenarios, e.g.completion of periodic structures. Our model generalizes surprisingly well to resolutions that are higher than thoseseen at train time, and achieves this at lower parameter & compute costs than the competitive baselines.

Big LaMa 🦙 (51M) results

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Distortions, Bokeh, Perspective

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title={Resolution-robust Large Mask Inpainting with Fourier Convolutions},
author={Suvorov, Roman and Logacheva, Elizaveta and Mashikhin, Anton and Remizova, Anastasia and Ashukha, Arsenii and Silvestrov, Aleksei and Kong, Naejin and Goka, Harshith and Park, Kiwoong and Lempitsky, Victor},
journal={arXiv preprint arXiv:2109.07161},

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