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