File size: 1,154 Bytes
6337268 e81f34c c1bd80f c7619da 6337268 e81f34c a788fe3 |
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 |
---
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/)
## 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}
}
``` |