Implementation of One Shot Face Swapping on Megapixels in PyTorch
- Please refer stylegan2-pytorch to get StyleGAN2 environment (converted model is provided in this repo)
- Python 3.6
- PyTorch 1.5.1 (it is ok that stylegan2-pytorch uses 1.3.1)
- CUDA 10.1/10.2
Please download CelebA-HQ and CelebAMask-HQ.
Also, please note the mask label is assigned as
| label list | ||
|---|---|---|
| 0: 'background' | 1: 'skin' | 2: 'l_brow' |
| 3: 'r_brow' | 4: 'l_eye' | 5: 'r_eye' |
| 6: 'eye_g' | 7: 'l_ear' | 8: 'r_ear' |
| 9: 'ear_r' | 10: 'nose' | 11: 'mouth' |
| 12: 'u_lip' | 13: 'l_lip' | 14: 'neck' |
| 15: 'neck_l' | 16: 'cloth' | 17: 'hair' |
| 18: 'hat' |
in case of updated CelebAMask-HQ dataset.
Baidu Cloud (access code: 7nov)
Put two provided files under stylegan2-pytorch directory, then run:
python inference.py \
--swap_type [ftm/injection/lcr] \
--img_root [CelebAHQ-PATH] \
--mask_root [CelebAMaskHQ-PATH] \
--srcID [INT-NUMBER] \
--tgtID [INT-NUMBER]
The result is rearrange as source_image, target_image, swapped_face, refined_swapped_face, where refined_swapped_face is the reconstructed version of swapped_face. Please refer more details in the provided codes.
- FTM
- ID Injection
- LCR
All the material, including source code, is made freely available for non-commercial use under the Creative Commons CC BY-NC 4.0 license. Feel free to use any of the material in your own work, as long as you give us appropriate credit by mentioning the title and author list of our paper:
@inproceedings{zhu2021megafs,
title={One Shot Face Swapping on Megapixels},
author={Zhu, Yuhao and Li, Qi and Wang, Jian and Xu, Chengzhong and Sun, Zhenan},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)},
pages = {4834-4844},
month = {June},
year={2021}
}