ML Anthology

Boundless: Generative Adversarial Networks for Image Extension

Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T. Freeman
ICCV 2019
doi:10.1109/ICCV.2019.01062 /iccv/2019/teterwak2019iccv-boundless/

Abstract

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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Cite

Text

Teterwak et al. "Boundless: Generative Adversarial Networks for Image Extension." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.01062

Markdown

[Teterwak et al. "Boundless: Generative Adversarial Networks for Image Extension." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/teterwak2019iccv-boundless/) doi:10.1109/ICCV.2019.01062

BibTeX

@inproceedings{teterwak2019iccv-boundless,
  title     = {{Boundless: Generative Adversarial Networks for Image Extension}},
  author    = {Teterwak, Piotr and Sarna, Aaron and Krishnan, Dilip and Maschinot, Aaron and Belanger, David and Liu, Ce and Freeman, William T.},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.01062},
  url       = {https://mlanthology.org/iccv/2019/teterwak2019iccv-boundless/}
}