---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
datasets:
- chuonghm/MaGGIe-HIM
metrics:
- mse
- sad
- mad
- conn
- grad
- dtssd
- messddt
pipeline_tag: image-segmentation
license: cc-by-4.0
---

# MaGGIe: Mask Guided Gradual Human Instance Matting

[[Project Page](https://maggie-matt.github.io/)] [[Code](https://github.com/hmchuong/MaGGIe)]

*Weights for Instance-awareness alpha human matting with binary mask guidance for images and video*

**Accepted at CVPR 2024**

**[Chuong Huynh](https://hmchuong.github.io/), [Seoung Wug Oh](https://sites.google.com/view/seoungwugoh/), [Abhinav Shrivastava](https://www.cs.umd.edu/~abhinav/), [Joon-Young Lee](https://joonyoung-cv.github.io/)**

Work is a part of Summer Internship 2023 at [Adobe Research](https://research.adobe.com/)

Please refer to our [paper](https://arxiv.org/abs/2404.16035) for details.

## Citation

If you find MaGGIe useful in your research, please cite the following paper:
```latex
@inproceedings{huynh2024maggie,
  title={Maggie: Masked guided gradual human instance matting},
  author={Huynh, Chuong and Oh, Seoung Wug and Shrivastava, Abhinav and Lee, Joon-Young},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3870--3879},
  year={2024}
}
```