Papers › Boundless: Generative Adversarial Networks for Image Extension

Boundless: Generative Adversarial Networks for Image Extension

19 Aug 2019ICCV 2019 10arXiv:1908.07007archive 2025-07-28

Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T. Freeman

Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation.

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Image InpaintingUncropping

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Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Uncropping Places2 val Boundless FID 11.8 #2 of 2 Archive leaderboard report
Uncropping Places2 val Boundless Fool rate 20.7 #2 of 2 Archive leaderboard report
Uncropping Places2 val Boundless PD 129.3 #2 of 2 Archive leaderboard report

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