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]
This project is for technical and academic use only. Please do not apply it to illegal and unethical scenarios.
If you find this project useful, please star it. It is the greatest appreciation of our work.
2021-08-30: Docker has been supported, please refer here for details.
2021-08-17: We have updated the Preparation, The main change is that the gpu version of onnx is now installed by default, Now the time to process a video is greatly reduced.
2021-07-19: Obvious border abruptness has been resolved. We add the ability to using mask and upgrade the old algorithm for better visual effect, please go to Inference for image or video face swapping for details. Please don’t forget to go to Preparation to check the latest set up. (Thanks for the help from @woctezuma and @instant-high)
2021-07-04: A new Colab performing multi specific face video swapping has been added. You can check it out here
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.
- python3.6+
- pytorch1.5+
- torchvision
- opencv
- pillow
- numpy
- imageio
- moviepy
- insightface
Inference for image or video face swapping
Colab fo switching specific faces in multi-face videos
Image face swapping demo & Docker image on Replicate
Training: coming soon
High-quality videos can be found in the link below:
[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
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)
For academic and non-commercial use only.The whole project is under the CC-BY-NC 4.0 license. See LICENSE for additional details.
@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}
}
Please visit our another ACMMM2020 high-quality style transfer project
Learn about our other projects [RainNet];