| --- |
| dataset_info: |
| features: |
| - name: video_id |
| dtype: string |
| - name: culture |
| dtype: string |
| - name: person |
| dtype: string |
| - name: action |
| dtype: string |
| - name: action_type |
| dtype: string |
| - name: location |
| dtype: string |
| - name: is_cross_culture |
| dtype: bool |
| - name: original_prompt |
| dtype: string |
| - name: refined_prompt |
| dtype: string |
| - name: video |
| dtype: video |
| - name: pipeline |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 676000000 |
| num_examples: 972 |
| download_size: 676000000 |
| dataset_size: 676000000 |
| language: |
| - en |
| license: cc0-1.0 |
| pretty_name: Multicultural Multiagent Videos |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - video-classification |
| --- |
| This dataset is part of the work presented in the paper [When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation](https://arxiv.org/abs/2605.16716). |
|
|
| The official code is available at [AIM-SCU/MAVEN](https://github.com/AIM-SCU/CRAFT). |
|
|
| # Multicultural Multiagent Videos Dataset |
|
|
| A comprehensive dataset of AI-generated videos showcasing multicultural content across different cultures, action types, and locations. The dataset includes videos generated using multiple pipeline approaches with both original and refined prompts. |
|
|
| ## Dataset Overview |
|
|
| - **Total Entries**: 972 (243 unique videos × 4 pipelines) |
| - **Cultures**: Chinese, American, Romanian |
| - **Mono-culture Videos**: 324 entries (81 unique videos × 4 pipelines) |
| - **Cross-culture Videos**: 648 entries (162 unique videos × 4 pipelines) |
| - **Action Types**: food, music, dance (324 entries each) |
| - **Pipelines**: base, sa (single-agent), mas (multi-agent sequential), map (multi-agent parallel) |
|
|
| ## Dataset Structure |
|
|
| Each video entry contains the following fields: |
|
|
| | Field | Type | Description | |
| |-------|------|-------------| |
| | `video_id` | string | Unique identifier for the video | |
| | `culture` | string | Primary culture represented in the video (Chinese, American, or Romanian) | |
| | `person` | string | Description of the person/character in the video | |
| | `action` | string | Description of the action being performed | |
| | `action_type` | string | Type of action: `food`, `dance`, or `music` | |
| | `location` | string | Location where the action takes place | |
| | `is_cross_culture` | bool | Whether the video represents cross-cultural content (mono=false, cross=true) | |
| | `original_prompt` | string | Original prompt used to generate the video | |
| | `refined_prompt` | string | Refined prompt with enhanced cultural details | |
| | `video_path` | string | Path to the video file relative to the results directory | |
| | `pipeline` | string | Pipeline used for generation: `base`, `sa`, `mas`, or `map` | |
|
|
| ## Pipelines |
|
|
| The dataset includes videos generated using four different pipeline approaches: |
|
|
| 1. **base** - Baseline single-agent approach |
| 2. **sa** - Single Agent pipeline |
| 3. **mas** - Multi-Agent Sequential pipeline |
| 4. **map** - Multi-Agent Parallel pipeline |
|
|
| ## Action Types |
|
|
| - **food**: Food preparation, eating, and culinary practices |
| - **dance**: Dancing and traditional dance performances |
| - **music**: Music playing and musical performances |
|
|
| ## Usage Example |
|
|
| ### Loading with Hugging Face Datasets |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset |
| dataset = load_dataset('guinea-pig/multicultural_multiagent_videos') |
| |
| # Access an example |
| example = dataset['train'][0] |
| print(f"Video ID: {example['video_id']}") |
| print(f"Culture: {example['culture']}") |
| print(f"Action Type: {example['action_type']}") |
| print(f"Original Prompt: {example['original_prompt']}") |
| print(f"Refined Prompt: {example['refined_prompt']}") |
| ``` |
|
|
| ### Filtering by Action Type |
|
|
| ```python |
| # Get only food-related videos |
| food_dataset = dataset.filter(lambda x: x['action_type'] == 'food') |
| |
| # Get only cross-cultural videos |
| cross_culture_dataset = dataset.filter(lambda x: x['is_cross_culture']) |
| |
| # Get videos from a specific culture |
| chinese_dataset = dataset.filter(lambda x: x['culture'] == 'Chinese') |
| ``` |
|
|
| ### Filtering by Pipeline |
|
|
| ```python |
| # Get videos from a specific pipeline |
| base_videos = dataset.filter(lambda x: x['pipeline'] == 'base') |
| sa_videos = dataset.filter(lambda x: x['pipeline'] == 'sa') |
| mas_videos = dataset.filter(lambda x: x['pipeline'] == 'mas') |
| map_videos = dataset.filter(lambda x: x['pipeline'] == 'map') |
| |
| # Get mono vs cross-culture videos |
| mono_culture = dataset.filter(lambda x: not x['is_cross_culture']) |
| cross_culture = dataset.filter(lambda x: x['is_cross_culture']) |
| ``` |
|
|
| ## Dataset Statistics |
|
|
| - **Action Type Distribution**: |
| - Food: 324 entries |
| - Music: 324 entries |
| - Dance: 324 entries |
|
|
| - **Culture Distribution**: |
| - Chinese: 324 entries |
| - American: 324 entries |
| - Romanian: 324 entries |
|
|
| - **Pipeline Distribution**: |
| - Each of the 4 pipelines: 243 entries |
| |
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{li2026when, |
| title = {When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation}, |
| author = {Li, Shuowei and Zhao, Yuming and Bhalerao, Parth and Ignat, Oana}, |
| booktitle = {Proceedings of the 5th Workshop on NLP for Positive Impact (NLP4PI)}, |
| year = {2026}, |
| address = {Budapest, Hungary}, |
| publisher = {Association for Computational Linguistics}, |
| url = {https://arxiv.org/abs/2605.16716} |
| } |
| ``` |
|
|
| ## License |
|
|
| This dataset is released under the CC0 license (public domain). |
|
|
| ## Notes |
|
|
| - Videos are stored in MP4 format |
| - Each video is exactly 5 seconds long |
| - Prompts have been refined to enhance cultural representation and detail |
| - The dataset supports research into culturally-aware video generation and representation |
|
|
