SimSwap: An Efficient Framework For High Fidelity Face Swapping

Proceedings of the 28th ACM International Conference on Multimedia

The official repository with Pytorch

Currently, only the test code is available, and training scripts are coming soon

[Arxiv paper]

[ACM DOI paper]

[Google Drive Paper link]

[Baidu Drive Paper link] Password: ummt

Results

Video

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

[Google Drive link for video 1]

[Google Drive link for video 2]

[Baidu Drive link for video] Password: b26n

[Online Video]

Dependencies

python3.6+
pytorch1.5+
torchvision
opencv
pillow
numpy

Usage

To test the pretrained model

python test_one_image.py --isTrain false  --name people --Arc_path arcface_model/arcface_checkpoint.tar --pic_a_path crop_224/6.jpg --pic_b_path crop_224/ds.jpg --output_path output/

--name refers to the SimSwap training logs name.

Pretrained model

Usage

There are two archive files in the drive: checkpoints.zip and arcface_checkpoint.tar

Copy the arcface_checkpoint.tar into ./arcface_model
Unzip checkpoints.zip, place it in the root dir ./

[Google Drive]

[Baidu Drive] Password: jd2v

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].