| --- |
| 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.* |
|
|
