license:cc-by-nc-2.0 size_categories:1K<n<10K format:imagefolder modality:image library:datasets library:mlcroissant arxiv:2312.06947 region:us
---
license: cc-by-nc-2.0
---


# CatMask-HQ
> **[ArXiv] MaTe3D: Mask-guided Text-based 3D-aware Portrait Editing**
> 
> [Kangneng Zhou](https://montaellis.github.io/), [Daiheng Gao](https://tomguluson92.github.io/), [Xuan Wang](https://xuanwangvc.github.io/), [Jie Zhang](https://scholar.google.com/citations?user=gBkYZeMAAAAJ), [Peng Zhang](https://scholar.google.com/citations?user=QTgxKmkAAAAJ&hl=zh-CN), [Xusen Sun](https://dblp.org/pid/308/0824.html), [Longhao Zhang](https://scholar.google.com/citations?user=qkJD6c0AAAAJ), [Shiqi Yang](https://www.shiqiyang.xyz/), [Bang Zhang](https://dblp.org/pid/11/4046.html), [Liefeng Bo](https://scholar.google.com/citations?user=FJwtMf0AAAAJ&hl=zh-CN), [Yaxing Wang](https://scholar.google.es/citations?user=6CsB8k0AAAAJ), [Yaxing Wang](https://scholar.google.es/citations?user=6CsB8k0AAAAJ), [Ming-Ming Cheng](https://mmcheng.net/cmm)

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.

 




<a href='https://montaellis.github.io/mate-3d/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> 
<a href='https://arxiv.org/abs/2312.06947'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> 
<a href='https://youtu.be/zMNYan1mIds'><img src='https://badges.aleen42.com/src/youtube.svg'></a> 
<a href='https://huggingface.co/datasets/Ellis/CatMaskHQ'><img src='https://img.shields.io/static/v1?label=Dataset&message=HuggingFace&color=yellow'></a> 
<a href='https://huggingface.co/Ellis/MaTe3D'><img src='https://img.shields.io/static/v1?label=Models&message=HuggingFace&color=yellow'></a> 




### Available sources
Please see Files and versions


### Contact

 [elliszkn@163.com](mailto:elliszkn@163.com) 



### 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}}


```

