---
pretty_name: VWG-Bench
language:
- en
task_categories:
- image-to-video
tags:
- video-generation
- reasoning
- benchmark
- image-to-video
- eccv-2026
license: cc-by-nc-4.0
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test/**
---

# VWG-Bench

VWG-Bench (Video World Generalist Benchmark) is an image-to-video reasoning
benchmark for evaluating whether video generation models can follow symbolic
rules, respect physical and commonsense constraints, and realize intended
goals over time.

The test split contains 380 initial images and structured annotations
organized into 9 reasoning dimensions and 38 ten-sample task groups.

## Data layout

```text
data/test/
├── metadata.jsonl
└── images/
    ├── 000000.png
    └── ...
taxonomy.json
evaluation_rules.json
schema.json
checksums.sha256
LICENSE.md
evaluation_code/
```

The layout follows the Hugging Face `ImageFolder` convention. Each
`metadata.jsonl` row has a `file_name` field relative to `data/test/`.
The `evaluation_code/` directory contains the cleaned VWG-Bench evaluation
toolkit and helper scripts.

## Annotation fields

| Field | Meaning |
|---|---|
| `id` | Stable sample identifier, 0–379 |
| `file_name` | Relative initial-image path |
| `dimension_id`, `dimension_name` | One of the 9 benchmark dimensions |
| `task_group_id` | Stable identifier for one of the 38 ten-sample groups |
| `task_group_name` | Current human-readable group name |
| `task_name` | Task label present in the source data |
| `user_prompt` | Image-to-video generation instruction |
| `last_frame_goal` | Expected final state |
| `progress_goal` | Optional expected intermediate process |
| `foreground_rule` | Optional foreground consistency constraint |
| `background_rule` | Optional background consistency constraint |
| `implicit_rule` | Optional implicit physical or logical constraint |
| `has_progress_goal` | Whether `progress_goal` is applicable |
| `image_*` | Image integrity and shape metadata |

## Evaluation

The bundled `evaluation_code/` package evaluates five 1–5 metrics when
applicable:

1. video quality;
2. progress consistency;
3. implicit-rule following;
4. progress-goal realization;
5. last-frame-goal realization.

Optional metrics are omitted when their annotation is `null`; they are not
treated as zero.

Minimal local validation after cloning the dataset repository:

```bash
cd evaluation_code
python -m pip install -e .
PYTHONPATH=src python -m vwg_bench.cli validate-data --dataset-root ..
```

To evaluate generated videos:

```bash
cd evaluation_code
export GEMINI_API_KEY="..."
bash scripts/eval_vwg.sh .. /path/to/generated_videos outputs/results.jsonl 0,1,2
```

Generated videos are expected by default as `{id}_seed{seed}.mp4`, for example
`0_seed0.mp4`. See `evaluation_code/README.md` for details and external
benchmark adapters.

## Dataset statistics

- Samples: 380
- Images: 380
- Reasoning dimensions: 9
- Stable task groups: 38
- Samples per task group: 10
- Split: test only
- Evaluation score range: integer 1–5

## License

VWG-Bench is released under the Creative Commons Attribution-NonCommercial
4.0 International License (CC BY-NC 4.0). See `LICENSE.md`.

## Citation

Citation metadata should be added after the camera-ready bibliographic
record is finalized.