Resolution-robust Large Mask Inpainting with Fourier Convolutions

Modern image inpainting systems, despite the significant progress, often struggle with large missing areas, complex geometric structures, and high-resolution images. Our model generalizes surprisingly well to resolutions that are higher than those seen at train time, and achieves this at lower parameter&time costs than the competitive baselines.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
• 2 To alleviate this issue, we propose a new method called large mask inpainting (LaMa).
• 3 LaMa is based on i) a new inpainting network architecture that uses fast Fourier convolutions (FFCs), which have the image-wide receptive field; ii) a high receptive field perceptual loss; iii) large training masks, which unlocks the potential of the first two components.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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