README.md

metadata

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.

The official code is available at AIM-SCU/MAVEN.

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

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

# 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

# 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

@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