It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the composite images are provided. We propose a framework called Gaussian-Poisson Generative Adversarial Network (GP-GAN) to leverage the strengths of the classical gradient-based approach and Generative Adversarial Networks. To the best of our knowledge, it's the first work that explores the capability of GANs in high-resolution image blending task. Concretely, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimization constrained by the gradient and color information. Inspired by the prior works, we obtain gradient information via applying gradient filters. To generate the color information, we propose a Blending GAN to learn the mapping between the composite images and the well-blended ones. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artifacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that the majority of workers are in favor of the proposed method.

Code and Extras

You can find the code on Github, including:
  • Train/test code (Chainer/Python)
  • Pre-trained models and datasets
  • Step-by-step tutorial
You can find an online demo here.


  title     = {GP-GAN: Towards Realistic High-Resolution Image Blending},
  author    = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi},
  booktitle = {ACMMM},
  year      = {2019}

Example Results (more results)

Supervised GP-GAN