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

[Conference paper]

Results

Video

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

[Baidu Drive link for video] Password: b26n

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 models/BEST_checkpoint.tar --pic_a_path crop_224/mars.jpg --pic_b_path crop_224/ds.jpg --output_path output/

--name refers to the checkpoint name.

Pretrained model

[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

Learn about our other projects [RainNet], [Sketch Generation], [CooGAN], [Knowledge Style Transfer], [SimSwap].

molo32 / SimSwap