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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- image-segmentation |
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tags: |
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- matting |
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- instance matting |
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- image matting |
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- video matting |
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- guidance matting |
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- human matting |
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pretty_name: MaGGIe - Human Instance Image and Video Matting |
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--- |
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<img src="maggie.png" alt="maggie" width="128"/> |
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# MaGGIe: Mask Guided Gradual Human Instance Matting |
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[[Project Page](https://maggie-matt.github.io/)] [[Code](https://github.com/hmchuong/MaGGIe)] |
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*Train datasets and Benchmarks for Instance-awareness alpha human matting with binary mask guidance for images and video* |
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**Accepted at CVPR 2024** |
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**[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/)** |
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Work is a part of Summer Internship 2023 at [Adobe Research](https://research.adobe.com/) |
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Please refer to our [paper](https://arxiv.org/abs/2404.16035) for details. |
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## Citation |
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If you find MaGGIe useful in your research, please cite the following paper: |
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```latex |
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@article{chuonghm_maggie, |
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author = {Chuong Huynh and Seoung Wug Oh and and Abhinav Shrivastava and Joon-Young Lee}, |
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title = {MaGGIe: Masked Guided Gradual Human Instance Matting}, |
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journal = {arXiv:2404.16035}, |
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year = {2024} |
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} |
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``` |
