CelebAMask-HQ

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.

• If you need the identity labels and the attribute labels of the images, please send request to the CelebA team.

• Demo of interactive facial image manipulation

Sample Images

Research Projects with CelebAMask-HQ

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

• Samples of interactive facial image manipulation

Downloads

• Google Drive: downloading link
• Baidu Drive: downloading link

Related work

• CelebA dataset :
  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 :
  Karras et. al, "Progressive Growing of GANs for Improved Quality, Stability, and Variation", in Internation Conference on Reoresentation Learning (ICLR), 2018

License and Citation

The use of this software is RESTRICTED to non-commercial research and educational purposes.

@article{CelebAMask-HQ,
  title={MaskGAN: Towards Diverse and Interactive Facial Image Manipulation},
  author={Cheng-Han Lee and Ziwei Liu and Lingyun Wu and Ping Luo},
  journal={Technical Report},
  year={2019}
}

The above work is still in submission.