Ellis/CatMaskHQ

CatMask-HQ

[ArXiv] MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing

Kangneng Zhou, Daiheng Gao, Xuan Wang, Jie Zhang, Peng Zhang, Xusen Sun, Longhao Zhang, Shiqi Yang, Bang Zhang, Liefeng Bo, Yaxing Wang, Yaxing Wang, Ming-Ming Cheng

To expand the scope beyond human face and explore the model generalization and expansion, we design the CatMask-HQ dataset with the following representative features:

Specialization: CatMask-HQ is specifically designed for cat faces, including precise annotations for six facial parts (background, skin, ears, eyes, nose, and mouth) relevant to feline features.

High-Quality Annotations: The dataset benefits from manual annotations by 50 annotators and undergoes 3 accuracy checks, ensuring high-quality labels and reducing individual differences.

Substantial Dataset Scale: With approximately 5,060 high-quality real cat face images and corresponding annotations, CatMask-HQ provides ample training database for deep learning models.

Available sources

Please see Files and versions

Contact

[email protected]

Citation

If you find this project helpful to your research, please consider citing:

@article{zhou2023mate3d,
  title     = {MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing},
  author    = {Kangneng Zhou, Daiheng Gao, Xuan Wang, Jie Zhang, Peng Zhang, Xusen Sun, Longhao Zhang, Shiqi Yang, Bang Zhang, Liefeng Bo, Yaxing Wang, Ming-Ming Cheng},
  journal   = {arXiv preprint arXiv:2312.06947},
  website   = {https://montaellis.github.io/mate-3d/},
  year      = {2023}}