---
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 834 video
clips paired with 1,500 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        |    304 |       850 |
| **Total**      |  **834** |  **1,500** |

## Files

- `vstat_qa_clean.json` — all 1,500 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
- `scripts/download_youtube.py` — fetches & trims the 304 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 nyu-visionx/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/nyu-visionx/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
@article{vstat2026,
    title={Benchmarking Visual State Tracking in Multimodal Video Understanding},
    author={Sihyun Yu and Nanye Ma and Pinzhi Huang and Hyunseok Lee and Shusheng Yang and June Suk Choi and Ellis Brown and Oscar Michel and Boyang Zheng and Jinwoo Shin and Saining Xie},
    year={2026},
    journal={arXiv preprint arXiv:2606.03920},
}
```