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Mask-Guided Portrait Editing with Conditional GANs

This is an official pytorch implementation of "Mask-Guided Portrait Editing with Conditional GANs"(CVPR2019). The major contributors of this repository include Shuyang Gu, Jianmin Bao, Hao Yang, Dong Chen, Fang Wen, Lu Yuan at Microsoft Research.

Introduction

Mask-Guided Portrait Editing is a novel technology based on mask-guided condititonal GANs, which can synthesize diverse, high-quality and controllable facial images from given masks. With the changeable input facial mask and source image, this method allows users to do high-level portrait editing.

Citation

If you find our code helpful for your research, please consider citing:

@inproceedings{gu2019mask,
  title={Mask-Guided Portrait Editing With Conditional GANs},
  author={Gu, Shuyang and Bao, Jianmin and Yang, Hao and Chen, Dong and Wen, Fang and Yuan, Lu},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3436--3445},
  year={2019}
} 

Getting Started

Prerequisite

  • Linux.
  • Pytorch 0.4.1.
  • Nvidia GPU: K40, M40, P100.
  • CUDA9.2 or 10.

Running code

  • download pretrained models here, put it under folder checkpoints/pretrained .
  • component editing: ./scripts/test_edit.sh
  • component transfer: ./scripts/test_edit_free_encode.sh change the corresponding component file in results/pretrained/editfree_latest, then run: ./scripts/test_edit_free_generate.sh get the component transfer results.
  • training: ./scripts/train.sh

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pytorch implementation of "Mask-Guided Portrait Editing with Conditional GANs"

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