wmpscc / StyleDNA

Kinship Face Synthesis

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StyleDNA: A High-Fidelity Age and Gender Aware Kinship Face Synthesizer

Description

This is the official repository of StyleDNA: A High-Fidelity Age and Gender Aware Kinship Face Synthesizer paper. (FG 2021) [colab demo]

Get Started

Prerequisites

  • Linux or macOS
  • Python3
  • PyTorch == 1.9.0+cu111
  • dlib == 19.22.1

Pretrained Model Weights

Inference Notebook

We provide a Jupyter notebook version running on Google Colab for fast inferecing.

Inferencing

Pretrained Weights

Download all the pretrained model weights and put them in ./pretrained_model/

Inferencing

Having your trained model weight, you can use ./inference.py to test the model on a set of images. For example,

python3 inference.py --mom_path ./test/mom.png --dad_path ./test/dad.png

Credits

We sincerely thanks for great development from other related projects, and we borrow code from

Citation

If you find this code useful for your research, please cite our paper, StyleDNA: A High-Fidelity Age and Gender Aware Kinship Face Synthesizer.

@inproceedings{lin2021styledna,
  title={StyleDNA: A High-Fidelity Age and Gender Aware Kinship Face Synthesizer},
  author={Lin, Che-Hsien and Chen, Hung-Chun and Cheng, Li-Chen and Hsu, Shu-Chuan and Chen, Jun-Cheng and Wang, Chih-Yu},
  booktitle={Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG)},
  year={2021}
}

About

Kinship Face Synthesis

License:MIT License


Languages

Language:Python 78.7%Language:Cuda 18.5%Language:C++ 2.8%