| --- |
| license: cc-by-nc-4.0 |
| pretty_name: Real-VAS |
| task_categories: |
| - image-segmentation |
| tags: |
| - amodal-segmentation |
| - video-segmentation |
| - occlusion |
| - video-understanding |
| - benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: occlusion |
| data_files: |
| - split: test |
| path: occlusion/**/annos.json |
| - config_name: containment |
| data_files: |
| - split: test |
| path: containment/**/annos.json |
| --- |
| |
| # Real-VAS: A Real-World Video Amodal Segmentation Dataset |
|
|
|  |
|
|
| Real-VAS is a zero-shot evaluation dataset for Video Amodal Segmentation, |
| built from composited real-world video. Every amodal ground-truth mask is |
| produced programmatically from an unoccluded source clip, so hidden object |
| boundaries are exact — no human guesswork, no multi-camera 3D rigs, no |
| synthetic appearance gap. |
|
|
| - **Project page:** https://finlay-hudson.github.io/real_vas |
| - **Code:** https://github.com/finlay-hudson/Real-VAS |
| - **Paper:** https://arxiv.org/abs/2611.00430 |
| |
| ## Dataset summary |
| |
| | | | |
| |---|---| |
| | Clips | 300 occlusion + 100 containment (400 total) | |
| | Occluded frames | 10,640 (72.5% prevalence) | |
| | Container types | On-Surface · Articulated · Mobile | |
| |
| ## Subsets |
| |
| - **`occlusion`** — two-source composites of a moving occluder over a moving occludee. Amodal masks come directly from the unoccluded source clip. |
| - **`containment`** — object entering a snug-fit container. Amodal masks are propagated through the container's tracked rigid/affine transform (validated to mean IoU 97.5%). |
| |
| ## Clip layout |
| |
| ``` |
| occlusion/<clip_name>/ |
| frames/ # per-frame RGB (JPG) |
| modal_masks/ # visible-only object mask (PNG, binary) |
| amodal_masks/ # full-object mask incl. hidden regions (PNG, binary) |
| annos.json # per-frame occlusion level + amount |
| form.json # clip-level metadata |
| containment/<clip_name>/ |
| (same structure) |
| ``` |
| |
| `annos.json` schema: |
| ```json |
| {"occlusion": [ |
| {"level": "NO_OCCLUSION" | "SLIGHT_OCCLUSION" | "SEVERE_OCCLUSION" | "NONE", |
| "amount": 0.0 - 1.0} |
| ]} |
| ``` |
| |
| ## Loading |
| |
| ### Annotations only (via `datasets`) |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("fhudson96/Real-VAS", name="occlusion") # or "containment" |
| print(ds["test"][0]) # one row per clip, column "occlusion" = list of per-frame annos |
| ``` |
| |
| ### Full clips (frames + masks + annos) |
| |
| ```python |
| from huggingface_hub import snapshot_download |
| from pathlib import Path |
| import json |
|
|
| local_dir = snapshot_download( |
| repo_id="fhudson96/Real-VAS", |
| repo_type="dataset", |
| allow_patterns="occlusion/**", # drop for everything; swap for "containment/**" |
| ) |
| |
| clip = Path(local_dir) / "occlusion" / "<clip_name>" |
| frames = sorted((clip / "frames").glob("*.png")) |
| amodal = sorted((clip / "amodal_masks").glob("*.png")) |
| modal = sorted((clip / "modal_masks").glob("*.png")) |
| annos = json.loads((clip / "annos.json").read_text()) |
| ``` |
| |
| ## Source data |
| |
| Occluder clips for the occlusion subset are drawn from publicly available YouTube videos (source video IDs are visible in clip names) plus in-house capture. All containment clips are in-house captures. Pipeline details — pose estimation, SAM2 masking, CoTracker3 motion filtering, Depth Anything V2 plausibility checks, Generative Omnimatte compositing — are in the paper. |
| |
| ## License |
| |
| Released under **CC-BY-NC-4.0**. Academic/research use is free with attribution. Commercial use requires written permission from the authors. |
| |
| ## Citation |
| |
| ```bibtex |
| @inproceedings{hudson2026realvas, |
| title = {Real-VAS: A Real-World Video Amodal Segmentation Dataset}, |
| author = {Hudson, Finlay G. C. and Smith, William A. P.}, |
| booktitle = {Under review}, |
| year = {2026} |
| } |
| ``` |
| |
