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README.md

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 license: cc-by-4.0
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+
+# BeyondMasks
+
+Dataset repository for **BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removal**, accepted to **ECCV 2026**.
+
+Authors: [Yiğit Ekin](https://openreview.net/profile?id=~Yi%C4%9Fit_Ekin1), [Enes Sanli](https://openreview.net/profile?id=~Enes_Sanli1), [Aykut Erdem](https://openreview.net/profile?id=~Aykut_Erdem1), [Erkut Erdem](https://openreview.net/profile?id=~Erkut_Erdem1), [Aysegul Dundar](https://openreview.net/profile?id=~Aysegul_Dundar1)
+
+BeyondMasks evaluates whether video object removal methods remove not only the target object, but also its causal and physical aftereffects such as shadows, reflections, and light effects.
+
+## Repository Contents
+
+```text
+BeyondMasks/
+├── masks/                # Binary mask videos indicating the object to remove
+├── object_not_present/   # Ground-truth videos where the object and aftereffects are absent
+├── objects_added/        # Input videos where the object and aftereffects are present
+├── data_example.json     # Example metadata format
+└── eval.py               # Proposed evaluation metric code
+```
+
+The three video folders are aligned by sample id. For example, `masks/1.mp4`, `object_not_present/1.mp4`, and `objects_added/1.mp4` correspond to the same sample.
+
+## Dataset Fields
+
+- `objects_added/`: input videos containing the target object and its physical aftereffects.
+- `object_not_present/`: reference videos where the target object and corresponding aftereffects are not present.
+- `masks/`: object masks for the region to remove.
+- `data_example.json`: example annotation entries with the sample id, foreground object, and aftereffect type.
+
+## Evaluation
+
+`eval.py` contains the proposed LLM-based evaluation metric. It scores object removal quality and aftereffect removal quality using the input video, ground-truth video, and a method output video. Adjust the folder paths according to your filesystem
+
+The script expects an evaluation root with this structure:
+
+```text
+eval_root/
+├── fg/       # Input videos with object and aftereffects present
+├── bg/       # Ground-truth videos with object and aftereffects absent
+├── result/   # Method output videos to evaluate
+└── data.json # Metadata annotations
+```
+
+Run:
+
+```bash
+export GEMINI_API_KEY=your_api_key
+python eval.py --root /path/to/eval_root
+```
+
+Results are written to `core_evaluation_results.json`.
+
+## License
+
+This dataset is released under the CC BY 4.0 license.
+
+## Citation
+
+Citation information will be added when the paper metadata is available.