EleGANt
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PyTorch code for "EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer" (ECCV 2022)
EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer
Official PyTorch implementation of ECCV 2022 paper "EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer"
Chenyu Yang, Wanrong He, Yingqing Xu, and Yang Gao.
Getting Started
- Installation
- Prepare Dataset & Checkpoints
Test
To test our model, download the weights of the trained model and run
python scripts/demo.py
Examples of makeup transfer results can be seen here.
Train
To train a model from scratch, run
python scripts/train.py
Customized Transfer
https://user-images.githubusercontent.com/61506577/180593092-ccadddff-76be-4b7b-921e-0d3b4cc27d9b.mp4
This is our demo of customized makeup editing. The interactive system is built upon Streamlit and the interface in ./training/inference.py
.
Controllable makeup transfer.
Local makeup editing.
Citation
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}
}
Acknowledgement
Some of the codes are build upon PSGAN and aster.Pytorch.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.