  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.
