Copyright (C) 2018 NVIDIA Corporation. All rights reserved. Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).

• Please check out our tutorial.

• For multimodal (or many-to-many) image translation, please check out our new work on MUNIT.

• 05-02-2018: We now adapt MUNIT code structure. For reproducing experiment results in the NIPS paper, please check out version_02 branch.

• 12-21-2017: Release pre-trained synthia-to-cityscape image translation model. See USAGE.md for usage examples.

• 12-14-2017: Added multi-scale discriminators described in the pix2pixHD paper. To use it simply make the name of the discriminator COCOMsDis.

Ming-Yu Liu, Thomas Breuel, Jan Kautz, "Unsupervised Image-to-Image Translation Networks" NIPS 2017 Spotlight, arXiv:1703.00848 2017

(We thank the Two Minute Papers channel for summarizing our work.)

More image results are available in the Google Photo Album.

Left: input. Right: neural network generated. Resolution: 640x480

Left: input. Right: neural network generated. Resolution: 640x480

• Snowy2Summery-01
• Snowy2Summery-02
• Day2Night-01
• Day2Night-02
• Translation Between 5 dog breeds
• Translation Between 6 cat species

From the first row to the fourth row, we show example results on day to night, sunny to rainy, summery to snowy, and real to synthetic image translation (two directions).

For each image pair, left is the input image; right is the machine generated image.