This is my reimplement of FaceShifter - a new SOTA in FaceSwapping using Deep Neural Network
I planed to train on three different datasets that was reported in the paper: CelebA, FFHQ and VGGFace.
I used FFHQ's script to detect and cropping faces in the images. In VGGFace, a large number of face has very small size and low resolution, thus I decided to only keep face crop images with size larger than a constant (i.e: h,w > 96).
python train.py
python test.py
python demo_image.py
https://github.com/taotaonice/FaceShifter
https://github.com/mindslab-ai/faceshifter
https://github.com/taesungp/contrastive-unpaired-translation
https://github.com/TreB1eN/InsightFace_Pytorch
@article{li2019faceshifter,
title={Faceshifter: Towards high fidelity and occlusion aware face swapping},
author={Li, Lingzhi and Bao, Jianmin and Yang, Hao and Chen, Dong and Wen, Fang},
journal={arXiv preprint arXiv:1912.13457},
year={2019}
}