# SimSwap: An Efficient Framework For High Fidelity Face Swapping
## Proceedings of the 28th ACM International Conference on Multimedia
**The official repository with Pytorch**
*Our method can realize **arbitrary face swapping** on images and videos with **one single trained model**.*
Currently, only the test code is available. Training scripts are coming soon
[](https://github.com/neuralchen/SimSwap)
Our paper can be downloaded from [[Arxiv]](https://arxiv.org/pdf/2106.06340v1.pdf) [[ACM DOI]](https://dl.acm.org/doi/10.1145/3394171.3413630)
## Top News
**`2021-06-20`**: We release the scripts for arbitrary video and image processing, and a colab demo.
## Dependencies
- python3.6+
- pytorch1.5+
- torchvision
- opencv
- pillow
- numpy
- imageio
- moviepy
- insightface
## Usage
[Preparation](./doc/guidance/preparation.md)
[Inference for image or video face swapping](./doc/guidance/usage.md)
[Colab demo](https://colab.research.google.com/github/neuralchen/SimSwap/blob/main/SimSwap%20colab.ipynb)
Training: **coming soon**
## Video
## Results


**High-quality videos can be found in the link below:**
[[Mama(video) 1080p]](https://drive.google.com/file/d/1JTruy6BTnT1EK1PSaZ4x-F8RhtZU_kT3/view?usp=sharing)
[[Google Drive link for video 1]](https://drive.google.com/file/d/1hdne7Gw39d34zt3w1NYV3Ln5cT8PfCNm/view?usp=sharing)
[[Google Drive link for video 2]](https://drive.google.com/file/d/1bDEg_pVeFYLnf9QLSMuG8bsjbRPk0X5_/view?usp=sharing)
[[Google Drive link for video 3]](https://drive.google.com/file/d/1oftHAnLmgFis4XURcHTccGSWbWSXYKK1/view?usp=sharing)
[[Baidu Drive link for video]](https://pan.baidu.com/s/1WTS6jm2TY17bYJurw57LUg ) Password: ```b26n```
[[Online Video]](https://www.bilibili.com/video/BV12v411p7j5/)
## User case
If you have some interesting results after using our project and are willing to share, you can contact us by email or share directly on the issue. Later, we may make a separate section to show these results, which should be cool.
At the same time, if you have suggestions for our project, please feel free to ask questions in the issue, or contact us directly via email: [email1](mailto:chenxuanhongzju@outlook.com), [email2](mailto:nicklau26@foxmail.com), [email3](mailto:ziangliu824@gmail.com). (All three can be contacted, just choose any one)
## To cite our paper
```
@inproceedings{DBLP:conf/mm/ChenCNG20,
author = {Renwang Chen and
Xuanhong Chen and
Bingbing Ni and
Yanhao Ge},
title = {SimSwap: An Efficient Framework For High Fidelity Face Swapping},
booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia},
pages = {2003--2011},
publisher = {{ACM}},
year = {2020},
url = {https://doi.org/10.1145/3394171.3413630},
doi = {10.1145/3394171.3413630},
timestamp = {Thu, 15 Oct 2020 16:32:08 +0200},
biburl = {https://dblp.org/rec/conf/mm/ChenCNG20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## Related Projects
**Please visit our another ACMMM2020 high-quality style transfer project**
[](https://github.com/neuralchen/ASMAGAN)
[](https://github.com/neuralchen/ASMAGAN)
Learn about our other projects
[[RainNet]](https://neuralchen.github.io/RainNet);
[[Sketch Generation]](https://github.com/TZYSJTU/Sketch-Generation-with-Drawing-Process-Guided-by-Vector-Flow-and-Grayscale);
[[CooGAN]](https://github.com/neuralchen/CooGAN);
[[Knowledge Style Transfer]](https://github.com/AceSix/Knowledge_Transfer);
[[SimSwap]](https://github.com/neuralchen/SimSwap);
[[ASMA-GAN]](https://github.com/neuralchen/ASMAGAN);
[[SNGAN-Projection-pytorch]](https://github.com/neuralchen/SNGAN_Projection)
[[Pretrained_VGG19]](https://github.com/neuralchen/Pretrained_VGG19).
## Acknowledgements
* [Insightface](https://github.com/deepinsight/insightface)