Towards the translation between Face <--> Sketch.
Download (photo+sketch+annotation): Google-drive, Baidu-disk, pw: FS2K.
For more details about the FS2K dataset, please visit this repo.
- Put the source frames and result images as follows:
FS2K_PROJ_EVAL
├── photo
│ ├── photo1
│ ├── photo2
│ └── photo3
├── sketch
│ ├── sketch1
│ ├── sketch2
│ └── sketch3
├── I2S
│ ├── results_method1
│ ├── results_method2
│ └── ...
├── S2I
│ ├── results_method1
│ ├── results_method2
│ └── ...
├── anno_test.json
├── anno_train.json
- Metric: SCOOT (designed for sketch evaluation) and SSIM.
- Env: MATLAB (tested with MATLAB R2020b)
- Run
eval_FSS_results.mfor testing. - Output: two excel files for saving SCOOT and SSIM scores of all methods: overall_scoot, scores_of_facial_parts.
@aticle{Fan2022FS2K,
title={Deep Facial Synthesis: A New Challenge},
author={Deng-Ping, Fan and Ziling, Huang and Peng, Zheng and Hong, Liu and Xuebin, Qin and Luc, Van Gool},
journal={Machine Intelligence Research},
year={2022}
}
@inproceedings{fan2019scoot,
title={Scoot: A perceptual metric for facial sketches},
author={Fan, Deng-Ping and Zhang, ShengChuan and Wu, Yu-Huan and Liu, Yun and Cheng, Ming-Ming and Ren, Bo and Rosin, Paul L and Ji, Rongrong},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={5612--5622},
year={2019}
}
@article{Wang2004ImageQA,
title={Image quality assessment: from error visibility to structural similarity},
author={Zhou Wang and Alan Conrad Bovik and Hamid R. Sheikh and Eero P. Simoncelli},
journal={IEEE Transactions on Image Processing},
year={2004},
volume={13},
pages={600-612}
}