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.
The dataset is built by our HairMapper method.
HairMapper: Removing Hair from Portraits Using GANs
Yiqian Wu, Yongliang Yang, Xiaogang Jin*.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
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.
Google drive link of the dataset will be released soon.
| dir | information |
|---|---|
| ├ origin | origin images, {img_id}.png |
| └ res | results images , {img_id}.png |
The non-hair-FFHQ dataset is available for non-commercial research purposes only.
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
Coming soon.