ErenAta00/VOID-Quadmask-Dataset

VOID-Compatible Quadmask Counterfactual Video Dataset

 ![Dataset Size]() ![Format]()

The first publicly available, pre-built quadmask-annotated counterfactual video dataset for physics-aware video object removal, inspired by and fully compatible with [Netflix/VOID](https://github.com/Netflix/void-model) (arXiv:2604.02296).

What is this?

VOID introduced a powerful framework for removing objects from videos while correcting downstream physical interactions. Their key innovation is the quadmask — a 4-value segmentation mask that tells the model which regions need physical correction after object removal.

While VOID released their model weights and generation code, they did not release pre-built training data. This dataset fills that gap by providing 200 ready-to-use counterfactual scene pairs with full quadmask annotations.

Dataset Structure

Each scene contains a counterfactual pair: the original video (with all objects) and the counterfactual video (with the target object removed and physics re-simulated).

scene_XXXX/
├── rgb_full.avi          # V: Original video (all objects present)
├── rgb_removed.avi       # V̂: Counterfactual video (target removed, physics re-run)
├── mask_lossless.avi     # Mq: Quadmask video (FFV1 lossless codec)
├── mask.mp4              # Mq: Quadmask video (MP4, quantized to exact values)
├── metadata.json         # Scene metadata and pipeline configuration
└── quadmask_frames/      # Individual quadmask frames as PNG
    ├── quadmask_0000.png
    ├── quadmask_0001.png
    └── ...

Quadmask Values (VOID-compatible)

Technical Specifications

Interaction Categories

This dataset includes 12 structured interaction categories, several of which go beyond the random-drop scenarios in Kubric:

⚙️ Quadmask Generation Pipeline

The affected-area masks are computed using a 4-method GPU ensemble:

• —Post-processing includes morphological cleanup, gridification (16px cells), and temporal smoothing (window=3).
• —Object mask (Mo) is derived from the V − V̂ difference in the first frame, before physics diverges — the only difference at t=0 is the removed object itself.

Generation Stack

• —Simulation: Unity 6 HDRP with deterministic PhysX
• —Quadmask Pipeline: Python + PyTorch on Google Colab
• —GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition (102GB VRAM)
• —Processing speed: 62 fps (v3.2), 200 scenes in 27 minutes
• —Success rate: 200/200 (0% failure)

Usage

from huggingface_hub import hf_hub_download
import cv2
import json

# Download a scene
scene = "scene_0000"
for f in ["rgb_full.avi", "rgb_removed.avi", "mask_lossless.avi", "metadata.json"]:
    hf_hub_download(
        repo_id="ErenAta00/VOID-Quadmask-Dataset",
        filename=f"{scene}/{f}",
        repo_type="dataset",
        local_dir="./void_data"
    )

# Read quadmask
cap = cv2.VideoCapture(f"./void_data/{scene}/mask_lossless.avi")
ret, frame = cap.read()
mask = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# mask values: 0 (object), 63 (overlap), 127 (affected), 255 (background)

Comparison with VOID Training Data

Citation

If you use this dataset, please cite both this dataset and the original VOID paper:

@misc{ata2026void_quadmask_dataset,
  title={VOID-Compatible Quadmask Counterfactual Video Dataset},
  author={Eren Ata},
  year={2026},
  url={[https://huggingface.co/datasets/ErenAta00/VOID-Quadmask-Dataset](https://huggingface.co/datasets/ErenAta00/VOID-Quadmask-Dataset)}
}

@misc{motamed2026void,
  title={VOID: Video Object and Interaction Deletion},
  author={Saman Motamed and William Harvey and Benjamin Klein and Luc Van Gool and Zhuoning Yuan and Ta-Ying Cheng},
  year={2026},
  eprint={2604.02296},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

License

Apache 2.0

🙏 Acknowledgments

This dataset was inspired by and built to be compatible with VOID by Netflix and INSAIT. We thank the VOID team for making their model and code openly available.