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GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

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Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending

Overview

sourcedestinationmaskcompositedblended

The author's implementation of GP-GAN, the high-resolution image blending algorithm described in:
"GP-GAN: Towards Realistic High-Resolution Image Blending"
Huikai Wu, Shuai Zheng, Junge Zhang, Kaiqi Huang

Given a mask, our algorithm can blend the source image and the destination image, generating a high-resolution and realsitic blended image. Our algorithm is based on deep generative models Wasserstein GAN.

Contact: Hui-Kai Wu (huikaiwu@icloud.com)

Citation

@article{wu2017gp,
  title   = {GP-GAN: Towards Realistic High-Resolution Image Blending},
  author  = {Wu, Huikai and Zheng, Shuai and Zhang, Junge and Huang, Kaiqi},
  journal = {ACMMM},
  year    = {2019}
}

Getting started

Train GP-GAN step by step

Train Blending GAN

Training Unsupervised Blending GAN

Visual results

MaskCopy-and-PasteModified-PoissonMulti-splinesSupervised GP-GANUnsupervised GP-GAN