---
license: cc-by-4.0
language:
- en
tags:
- video
- multimodal
- benchmark
- video-question-answering
- visual-state-tracking
size_categories:
- 1K<n<10K
---

# VSTAT: Visual State Tracking Benchmark

VSTAT is a video-based benchmark for evaluating the **visual state tracking**
capability of Multimodal Large Language Models (MLLMs). It contains 813 video
clips paired with 1,479 questions whose answers cannot be inferred from any
single keyframe or short segment.

## Dataset Composition

| Split          | Videos | Questions |
|----------------|-------:|----------:|
| synthetic      |    450 |       550 |
| self_recorded  |     80 |       100 |
| youtube        |    283 |       830 |
| **Total**      |  **813** |  **1,479** |

## Files

- `vstat_qa_clean.json` — all 1,479 question-answer pairs with taxonomy labels
- `youtube_metadata.json` — YouTube URLs + start/end timestamps (one entry per chunk)
- `youtube_resolutions.json` — per-clip target (W, H, fps) used by the
  downloader to reproduce the official release pixel layout
- `redactions.json` — declarative privacy-redaction regions applied after trim
- `croissant.json` — Croissant 1.0 metadata (with RAI extension)
- `scripts/download_youtube.py` — fetches & trims the 283 YouTube clips
- `scripts/redact.sh` — applies the privacy black-boxes from `redactions.json`
- `scripts/build_resolution_map.py` — utility to (re)build `youtube_resolutions.json`
  from a reference render
- `videos/synthetic/<category>/<id>.mp4` — Blender-rendered videos (hosted)
- `videos/self_recorded/<category>/<id>.mp4` — author-recorded clips, hands only,
  audio removed (hosted)
- `videos/youtube/<category>/<id>.mp4` — **NOT redistributed**; you must
  download these yourself with the provided script (see *Quick start* below)

## Quick start

### 1. Get the repo

Pick whichever method you prefer:

```bash
# A. huggingface-cli (recommended, supports LFS)
pip install -U "huggingface_hub[cli]"
huggingface-cli download VSTAT-NeurIPS2026/VSTAT \
  --repo-type=dataset \
  --local-dir vstat
cd vstat

# B. git clone (requires git-lfs installed)
git lfs install
git clone https://huggingface.co/datasets/VSTAT-NeurIPS2026/VSTAT vstat
cd vstat
```

After this, you have all annotations and the synthetic + self_recorded
videos. The YouTube clips are still missing — fetch them next.

### 2. Download and redact the YouTube clips

The downloader reads `youtube_metadata.json` and downloads each source
video once with `yt-dlp`, then trims it into the chunks expected by
`vstat_qa_clean.json`. Pass `--resolution-map youtube_resolutions.json`
so each chunk lands at the exact `(width, height, fps)` of the official
release. After trimming, `scripts/redact.sh` applies the privacy
black-boxes (matches `redactions.json`) to the affected clips in place.

> **Important — reproducing the official release.** The benchmark
> numbers in our paper were obtained on the clips produced by exactly
> this two-step pipeline (`download_youtube.py --resolution-map …` →
> `redact.sh`). The downloader picks the smallest YouTube format that
> matches each clip's target dimensions and frame rate so the trim
> avoids any resampling drift. Skip the resolution map only for
> ablations on input resolution.

```bash
# Install dependencies
pip install -U yt-dlp
# macOS:    brew install ffmpeg
# Ubuntu:   sudo apt install ffmpeg

# 1. Fetch and trim every YouTube clip to its release-spec dims
python scripts/download_youtube.py --resolution-map youtube_resolutions.json

# 2. Apply privacy redactions in place (idempotent)
bash scripts/redact.sh
```

Common flags for the downloader:

```bash
# Faster: 4 parallel downloads
python scripts/download_youtube.py --resolution-map youtube_resolutions.json --workers 4

# Test on a few videos first
python scripts/download_youtube.py --resolution-map youtube_resolutions.json --limit 5

# Keep the full source videos around (faster re-trim, more disk)
python scripts/download_youtube.py --resolution-map youtube_resolutions.json --keep-fulls

# Print plan without doing anything
python scripts/download_youtube.py --resolution-map youtube_resolutions.json --dry-run

# Cap source download size (default uncapped — required for portrait sources)
python scripts/download_youtube.py --resolution-map youtube_resolutions.json --source-cap 1080
```

Re-running the downloader is safe: it skips clips that already exist
on disk and writes a `download_report.json` listing any failures (rare,
usually due to YouTube link rot — affected clips can be reported to
the authors via the dataset issue tracker). Re-running `redact.sh` is
also idempotent and replaces any earlier redaction with the canonical
set defined in `redactions.json`.

### 3. Load the data

```python
import json

with open("vstat_qa_clean.json") as f:
    data = json.load(f)

for cat, entries in data["data"].items():
    for e in entries:
        print(e["video_id"], e["video_path"], e["video_source"])
```

Each entry has these fields:

| Field                      | Description                                                 |
|----------------------------|-------------------------------------------------------------|
| `video_id`                 | Unique identifier (e.g. `0001_pt1_q1`)                      |
| `video_path`               | Relative path under `videos/`                               |
| `video_source`             | `synthetic` / `self_recorded` / `youtube`                   |
| `source_task`              | Coarse category (e.g. `basketball`, `dice`, `shell_game`)   |
| `question`                 | Question text. For MCQ items, choices are inline `(A)(B)…`  |
| `answer_type`              | `mcq` or `numeric`                                          |
| `answer`                   | Letter (`A`/`B`/`C`/`D`) for MCQ; integer for numeric       |
| `choices`                  | List of MCQ option strings (empty for numeric)              |
| `answer_index`             | 0-based index into `choices` (null for numeric)             |
| `perceptual_complexity`    | List of perceptual challenge tags (see Taxonomy)            |
| `state_element_type`       | `count` / `location` / `attribute`                          |
| `state_structure`          | `atomic` / `sequence` / `set` / `dictionary`                |
| `youtube_url`, `youtube_id`, `start_time`, `end_time`, `start_sec`, `end_sec` | Present only for `video_source == "youtube"` |

### 4. Run an evaluation

A minimal MCQ scoring loop (numeric questions are scored with mean
relative accuracy in our paper; see Section 3.1 for details):

```python
def score(entry, model_pred):
    if entry["answer_type"] == "mcq":
        return int(model_pred.strip().upper() == entry["answer"])
    # numeric
    try:
        return int(int(model_pred) == int(entry["answer"]))
    except ValueError:
        return 0
```

## Taxonomy

Each question is annotated with:

- `perceptual_complexity` (multi-label, paper Section 2.2):
  `action_ambiguity`, `camera_motion`, `homogeneity`,
  `multi_entity_attribution`, `occlusion`, `symbolic_decoding`
- `state_element_type` (single label): `count`, `location`, `attribute`
- `state_structure` (single label): `atomic`, `sequence`, `set`, `dictionary`

## License

- Annotations and self-recorded / synthetic videos: **CC BY 4.0**
- YouTube videos: NOT redistributed; subject to original uploader's license
- See `LICENSE` for full terms

## Privacy & consent

- Self-recorded videos contain only the authors' hands; no faces, voices,
  or other identifiable persons. Audio tracks were stripped before release.
- Authors consented to public release of their hand footage.
- For YouTube clips, only URLs and timestamps are redistributed; original
  uploaders retain control over their content. The `redact.sh` step
  applies black-boxes over scoreboards / on-screen text in a small
  number of clips per `redactions.json`, matching the official release.

## Citation

```bibtex
@inproceedings{vstat2026,
  title={Benchmarking State Tracking in Multimodal Video Understanding},
  author={Anonymous},
  booktitle={NeurIPS 2026 Datasets and Benchmarks Track},
  year={2026}
}
```

*This is a NeurIPS 2026 anonymous submission. Author names will be added upon acceptance.*