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.

• Installation
• Prepare Dataset & Checkpoints

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.

To train a model from scratch, run

python scripts/train.py

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.

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 upon PSGAN and aster.Pytorch.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.