| --- |
| 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. |
|
|
