79 lines
2.6 KiB
Markdown
79 lines
2.6 KiB
Markdown
# [CVPR2024] Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth
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Zhaoyang Sun, Shegnwu Xiong, Yaxiong Chen, Yi Rong
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## Paper link
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https://arxiv.org/abs/2405.17240
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## News
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+ Our recent paper “SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion Models” has been accepted by NeurIPS 2024 and will be released later.
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## Framework
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## Requirements
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We recommend that you just use your own pytorch environment; the environment needed to run our model is very simple. If you do so, please ignore the following environment creation.
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A suitable [conda](https://conda.io/) environment named `CSDMT` can be created
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and activated with:
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```
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conda env create -f environment.yaml
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conda activate CSDMT
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```
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## Pretrained Models
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Pre-training models can be downloaded here [Baidu Drive](https://pan.baidu.com/s/1C7K4xk5W0X65yUQh41AmfQ),password: 1d3e and [Google Drive](https://drive.google.com/drive/folders/1pvSgkpsb7k6Ph1_oCmFkMQZPgL7PaTO0?usp=drive_link)
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Please put CSD_MT.pth in CSD_MT/weights/CSD_MT.pth
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Put lms.dat in faceutils/dlibutils/lms.dat
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Put 79999_iter.pth in facultyils/face_parsing/res/cp/79999 _iter.pth
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## Run the examples
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The computational complexity and computational resources required for our model are also small, and we only tested the code on CPU.
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In the current directory, run the following command
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```
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python gradio_makeup_transfer.py
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```
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## How to run your own data
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Just upload your own images in the above gradio screen.
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## Citation
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If this work is helpful for your research, please consider citing the following BibTeX entry.
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```text
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@inproceedings{sun2024content,
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title={Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth},
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author={Sun, Zhaoyang and Xiong, Shengwu and Chen, Yaxiong and Rong, Yi}
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journal={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
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year={2024}
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}
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```
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## Acknowledgement
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Some of the codes are build upon [PSGAN](https://github.com/wtjiang98/PSGAN), [Face Parsing](https://github.com/zllrunning/face-parsing.PyTorch) and [aster.Pytorch](https://github.com/ayumiymk/aster.pytorch).
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## License
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This work is licensed under a
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[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
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[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
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[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
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[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
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[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
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