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non-hair-FFHQ

The non-hair-FFHQ dataset is a high-quality image dataset that contains 6,000 non-hair FFHQ portraits, based on stylegan2-ada and ffhq-dataset.

non-hair-FFHQ

The dataset is built by our HairMapper method.

HairMapper: Removing Hair from Portraits Using GANs<br> Yiqian Wu, Yongliang Yang, Xiaogang Jin*.<br>2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

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Project Paper Suppl Video Dataset Github

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We apply our method on FFHQ images (all images have licenses that allow free use, redistribution, and adaptation for non-commercial purposes) and present a non-hair-FFHQ dataset that contains 6,000 non-hair portraits to inspire and facilitate more works in the future.

Overview

Google drive link of the dataset : https://drive.google.com/drive/folders/1CbyFYDTUqWRneyuDlVznY4XG-8pLhoAS?usp=sharing.

dirinformation
hairoriginal images, {img_id}.png
non-hairresults images , {img_id}.png

Code

https://github.com/oneThousand1000/HairMapper

Agreement

The non-hair-FFHQ dataset is available for non-commercial research purposes only.

Related Works

A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras (NVIDIA), Samuli Laine (NVIDIA), Timo Aila (NVIDIA) https://arxiv.org/abs/1812.04948

Training Generative Adversarial Networks with Limited Data Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, Timo Aila https://arxiv.org/abs/2006.06676

Citation

@InProceedings{Wu_2022_CVPR,
    author    = {Wu, Yiqian and Yang, Yong-Liang and Jin, Xiaogang},
    title     = {HairMapper: Removing Hair From Portraits Using GANs},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {4227-4236}
}

Contact

jin@cad.zju.edu.cn

onethousand@zju.edu.cn

onethousand1250@gmail.com