Chenyu-Yang-2000/EleGANt 开源项目中文解读：技术栈、适用场景与上手建议

Chenyu-Yang-2000

项目介绍

Chenyu-Yang-2000/EleGANt 是一个托管在 GitHub 上的开源项目，主要使用 Python 编写，采用 Other 许可证，目前获得 185 个 Star、38 次 Fork。

仓库全名: Chenyu-Yang-2000/EleGANt

README 摘要

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 Examples of makeup transfer results can be seen here…

完整内容见 GitHub 原始 README 或本页。

常见问题

EleGANt 用什么语言开发？

主要使用 Python 编写，采用 Other 许可证，目前有 185 个 Star。

EleGANt 可以免费商用吗？

仓库标注的许可证为 Other，商用前请先阅读仓库 LICENSE 文件确认授权范围。

如何获取 EleGANt 的源码？

可以直接克隆仓库：git clone https://github.com/Chenyu-Yang-2000/EleGANt.git，默认分支为 main；安装与运行步骤见仓库 README。

EleGANt 最近还在维护吗？

仓库最近一次更新为 2022年7月23日，当前有 18 个未关闭 Issue；Star 数为 185，可作为社区活跃度的参考。

关于

PyTorch code for "EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer" (ECCV 2022)

elegant gan makeup-transfer pytorch makeup eccv

Other
185 Stars
38 Forks
2 Watching

描述取自 GitHub 官方 API，中文内容由 GitRead 中文社区编辑团队整理。

简要分析

热度分

263

价值分

121

活跃状态

活跃

GitHub

语言占比

Python

贡献者

Chenyu-Yang-2000

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.

teaser

Getting Started

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.

control

Local makeup editing.

edit

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.

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