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

Our paper can be downloaded from [Arxiv] [ACM DOI]

Attention

This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.

Top News

2021-07-03: We add the scripts for multi specific face swapping, please go to Inference for image or video face swapping for details.

2021-07-02: We add the scripts for designating a specific person in arbitrary video or image to change face, please go to Inference for image or video face swapping for details.

2021-07-02: We have added a hyper parameter to allow users to choose whether to add the simswap logo as a watermark, please go to the section "About watermark of simswap logo" of Inference for image or video face swapping for details.

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

Inference for image or video face swapping

Colab demo

Training: coming soon

Video

Results

High-quality videos can be found in the link below:

[Mama(video) 1080p]

[Google Drive link for video 1]

[Google Drive link for video 2]

[Google Drive link for video 3]

[Baidu Drive link for video] Password: b26n

[Online Video]

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, email2, email3. (All three can be contacted, just choose any one)

License

For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See LICENSE for additional details.

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

Learn about our other projects [RainNet];

[Sketch Generation];

[CooGAN];

[Knowledge Style Transfer];

[SimSwap];

[ASMA-GAN];

[SNGAN-Projection-pytorch]

[Pretrained_VGG19].

Acknowledgements

• Insightface