OfficialPyTorchimplementation of ECCV 2022 paper "EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer"
Chenyu Yang, Wanrong He, Yingqing Xu, and Yang Gao.
To test our model, download theweightsof the trained model and run
python scripts/demo.py
Examples of makeup transfer results can be seenhere.
To train a model from scratch, run
python scripts/train.py
Customized_transfer.mp4
This is our demo of customized makeup editing. The interactive system is built uponStreamlitand the interface in./training/inference.py
.
Controllable makeup transfer.
Local makeup editing.
If this work is helpful for your research, please consider citing the following BibTeX entry.
@article{yang2022elegant,
title={EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer},
author={Yang, Chenyu and He, Wanrong and Xu, Yingqing and Gao, Yang}
journal={arXiv preprint arXiv:2207.09840},
year={2022}
}
Some of the codes are build uponPSGANandaster.Pytorch.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.