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---
license: cc-by-nc-4.0
task_categories:
- image-segmentation
tags:
- matting
- instance matting
- image matting
- video matting
- guidance matting
- human matting
pretty_name: MaGGIe - Human Instance Image and Video Matting
---
<img src="maggie.png" alt="maggie" width="128"/>
# MaGGIe: Mask Guided Gradual Human Instance Matting
[[Project Page](https://maggie-matt.github.io/)] [[Code](https://github.com/hmchuong/MaGGIe)]
*Train datasets and Benchmarks 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
@article{chuonghm_maggie,
author = {Chuong Huynh and Seoung Wug Oh and and Abhinav Shrivastava and Joon-Young Lee},
title = {MaGGIe: Masked Guided Gradual Human Instance Matting},
journal = {arXiv:2404.16035},
year = {2024}
}
``` |