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chuonghm/MaGGIe-HIM

The MaGGIe dataset is a training and benchmark collection for instance‑aware alpha portrait matting in images and videos, specifically targeting mask‑guided matting tasks. Developed during Adobe Research’s 2023 Summer Internship and accepted at CVPR 2024, it serves applications such as image segmentation, instance matting, portrait matting, video matting, guided matting, and human matting.

Updated 6/14/2024
hugging_face

Description

MaGGIe: Mask Guided Gradual Human Instance Matting

Dataset Overview

  • Name: MaGGIe – Human Instance Image and Video Matting
  • Task Category: Image Segmentation
  • Tags:
    • matting
    • instance matting
    • image matting
    • video matting
    • guidance matting
    • human matting
  • License: CC‑BY‑NC‑4.0

Detailed Description

  • Project: A training and benchmark dataset for binary‑mask‑guided gradual human instance matting in images and videos.
  • Status: Accepted at CVPR 2024.

Authors

  • Chuong Huynh
  • Seoung Wug Oh
  • Abhinav Shrivastava
  • Joon‑Young Lee

Citation

@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}
}

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Topics

Image Segmentation
Human Instance Segmentation

Source

Organization: hugging_face

Created: Unknown

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