BeyondMasks

Dataset repository for BeyondMasks: Evaluating Causal and Physical Consistency in Video Object Removal, accepted to ECCV 2026.

Authors: Yiğit Ekin, Enes Sanli, Aykut Erdem, Erkut Erdem, Aysegul Dundar

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

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:

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:

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.