EffectErase / README.md
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license: cc-by-nc-4.0
tags:
  - video generation
  - video edit
  - object remove
extra_gated_heading: Review and accept the license to access the EffectErase dataset
extra_gated_description: >
  This dataset is restricted to non-commercial research use. License terms have
  been updated. If you requested access before March 20, 2026, 20:23 (AoE),
  please resubmit your request.
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  confirm that you will use this dataset for non-commercial purposes only, will
  not redistribute the raw data or any substantial portion of it, and will
  properly cite EffectErase (CVPR 2026) in any publication or project that uses
  this dataset. License terms were recently updated; prior approvals may require
  resubmission. For any commercial use, please contact the authors for a
  separate license.
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EffectErase Dataset

🚨 License Update Notice (Important)
We have updated the dataset license and access terms.
If you submitted an access request before March 20, 2026, 20:23 (AoE),
please resubmit your request to acknowledge the updated license.


⚠️ This dataset is for non-commercial research purposes only.
Commercial use is strictly prohibited without explicit permission from the authors.


Overview

The EffectErase dataset is designed for video object removal and visual effect erasing tasks.
It supports research on removing objects together with their associated visual effects such as shadows, reflections, and lighting artifacts.


License

This dataset is released under the CC BY-NC 4.0 License.

  • ✔ Allowed: Non-commercial research use
  • ❌ Not allowed: Any commercial use
  • ❌ Not allowed: Redistribution of raw data

For any commercial usage, please contact the authors for a separate license.


Usage

Please ensure that your use of this dataset complies with:

  • Applicable laws and regulations
  • Ethical research standards
  • The license terms stated above

Citation

If you use this dataset, please cite:

@inproceedings{fu2026EffectErase,
  title={{EffectErase}: Joint Video Object Removal and Insertion for High-Quality Effect Erasing},
  author={Fu, Yang and Zheng, Yike and Dai, Ziyun and Ding, Henghui},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2026}
}