Abstract:
CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA. The masks of CelebAMask-HQ were manually-annotated with the size of 512 x 512 and 19 classes including all facial components and accessories such as skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, and cloth. CelebAMask-HQ can be used to train and evaluate algorithms of face parsing, face recognition, and GANs for face generation and editing. ## Sample Images https://raw.githubusercontent.com/switchablenorms/CelebAMask-HQ/master/images/sample.png ## Face Manipulation Model with CelebAMask-HQ CelebAMask-HQ can be used on several research fields including: facial image manipulation, face parsing, face recognition, and face hallucination. We showcase an application on interactive facial image manipulation as bellow: * Samples of interactive facial image manipulation ## Related Works * **CelebA** dataset:<br/> Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang, "Deep Learning Face Attributes in the Wild", in IEEE International Conference on Computer Vision (ICCV), 2015 * **CelebA-HQ** was collected from CelebA and further post-processed by the following paper :<br/> Karras et. al, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", in Internation Conference on Reoresentation Learning (ICLR), 2018 ## Dataset Agreement * The CelebAMask-HQ dataset is available for **non-commercial research purposes** only. * You agree **not to** reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data. * You agree **not to** further copy, publish or distribute any portion of the CelebAMask-HQ dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset. ## Related Projects using CelebAMask-HQ * [SPADE-TensorFlow](https://github.com/taki0112/SPADE-Tensorflow) * [FaceParsing-PyTorch](https://github.com/zllrunning/face-parsing.PyTorch) ## License and Citation The use of this software is RESTRICTED to **non-commercial research and educational purposes**. ``` @inproceedings{CelebAMask-HQ, title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation}, author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping}, booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2020} } ```

URL: https://github.com/switchablenorms/CelebAMask-HQ
License: No license specified, the work may be protected by copyright.

@article{,
title= {CelebAMask-HQ},
keywords= {Face, celeb, celeba},
author= {},
abstract= {CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA.

The masks of CelebAMask-HQ were manually-annotated with the size of 512 x 512 and 19 classes including all facial components and accessories such as skin, nose, eyes, eyebrows, ears, mouth, lip, hair, hat, eyeglass, earring, necklace, neck, and cloth.

CelebAMask-HQ can be used to train and evaluate algorithms of face parsing, face recognition, and GANs for face generation and editing.

## Sample Images

https://raw.githubusercontent.com/switchablenorms/CelebAMask-HQ/master/images/sample.png

## Face Manipulation Model with CelebAMask-HQ
CelebAMask-HQ can be used on several research fields including: facial image manipulation, face parsing, face recognition, and face hallucination. We showcase an application on interactive facial image manipulation as bellow:

* Samples of interactive facial image manipulation

## Related Works

* **CelebA** dataset:<br/>
Ziwei Liu, Ping Luo, Xiaogang Wang and Xiaoou Tang, "Deep Learning Face Attributes in the Wild", in IEEE International Conference on Computer Vision (ICCV), 2015 
* **CelebA-HQ** was collected from CelebA and further post-processed by the following paper :<br/>
Karras et. al, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", in Internation Conference on Reoresentation Learning (ICLR), 2018

## Dataset Agreement
* The CelebAMask-HQ dataset is available for **non-commercial research purposes** only.
* You agree **not to** reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
* You agree **not to** further copy, publish or distribute any portion of the CelebAMask-HQ dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

## Related Projects using CelebAMask-HQ
* [SPADE-TensorFlow](https://github.com/taki0112/SPADE-Tensorflow)
* [FaceParsing-PyTorch](https://github.com/zllrunning/face-parsing.PyTorch)

## License and Citation
The use of this software is RESTRICTED to **non-commercial research and educational purposes**.
```
@inproceedings{CelebAMask-HQ,
  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
  author={Lee, Cheng-Han and Liu, Ziwei and Wu, Lingyun and Luo, Ping},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2020}
}
```

},
terms= {},
license= {},
superseded= {},
url= {https://github.com/switchablenorms/CelebAMask-HQ}
}