Papers › Boundless: Generative Adversarial Networks for Image Extension
Boundless: Generative Adversarial Networks for Image Extension
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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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