File size: 2,873 Bytes
ecd4446 5c8442a ecd4446 5c8442a ecd4446 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
---
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
[[email protected]](mailto:[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}}
```
|