Recent advances in generative adversarial networks (GANs) have shown promising potentials in conditional image generation. However, how to generate high-resolution images remains an open problem. In this paper, we aim at generating high-resolution well-blended images given composited copy-and-paste ones, i.e. realistic high-resolution image blending. To achieve this goal, we propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths of classical gradient-based approaches and GANs, which is the first work that explores the capability of GANs in high-resolution image blending task to the best of our knowledge. Particularly, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimisation constrained by the gradient and colour information. Gradient filters can obtain gradient information. For generating the colour information, we propose Blending GAN to learn the mapping between the composited image and the well-blended one. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artefacts. 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 majority of workers are in favour of the proposed approach.

Code and Extras

You can find the code on Github, including:
  • Training/test code (uses Chainer/Python)
  • Pretrained models and datasets
  • Step-by-step tutorial to run our algorithm


  title={GP-GAN: Towards Realistic High-Resolution Image Blending},
  author={Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi},
  journal={arXiv preprint arXiv:1703.07195},

Example Results (more results)

Supervised GP-GAN
Unsupervised GP-GAN