danielrosehill/multimodal-ai-taxonomy

Multimodal AI Taxonomy

A comprehensive, structured taxonomy for mapping multimodal AI model capabilities across input and output modalities.

Dataset Description

This dataset provides a systematic categorization of multimodal AI capabilities, enabling users to:

• Navigate the complex landscape of multimodal AI models
• Filter models by specific input/output modality combinations
• Understand the nuanced differences between similar models (e.g., image-to-video with/without audio, with/without lip sync)
• Discover models that match specific use case requirements

Dataset Summary

The taxonomy organizes multimodal AI capabilities by:

• Output modality (video, audio, image, text, 3D models)
• Operation type (creation vs. editing)
• Detailed characteristics (lip sync, audio generation method, motion type, etc.)
• Maturity level (experimental, emerging, mature)
• Platform availability and example models

Supported Tasks

This is a reference taxonomy dataset for:

• Model discovery and filtering
• Understanding multimodal AI capabilities
• Research into multimodal AI landscape
• Building model selection tools

Dataset Structure

The dataset is provided as JSONL files (JSON Lines format) for efficient loading:

data/
├── train.jsonl                          # Complete dataset
├── taxonomy_video_creation.jsonl        # Video creation modalities
├── taxonomy_video_editing.jsonl         # Video editing modalities
├── taxonomy_audio_creation.jsonl        # Audio creation modalities
├── taxonomy_audio_editing.jsonl         # Audio editing modalities
├── taxonomy_image_creation.jsonl        # Image creation modalities
├── taxonomy_image_editing.jsonl         # Image editing modalities
└── taxonomy_3d-model_creation.jsonl     # 3D creation modalities

Source taxonomy files (used for generation):

taxonomy/
├── schema.json                          # Common schema definition
├── README.md                            # Taxonomy documentation
├── video-generation/
│   ├── creation/modalities.json
│   └── editing/modalities.json
├── audio-generation/
│   ├── creation/modalities.json
│   └── editing/modalities.json
├── image-generation/
│   ├── creation/modalities.json
│   └── editing/modalities.json
├── text-generation/
│   ├── creation/modalities.json
│   └── editing/modalities.json
└── 3d-generation/
    ├── creation/modalities.json
    └── editing/modalities.json

Data Instances

Each modality entry in the JSONL files contains flattened fields:

{
  "id": "img-to-vid-lipsync-text",
  "name": "Image to Video (Lip Sync from Text)",
  "input_primary": "image",
  "input_secondary": ["text"],
  "output_primary": "video",
  "output_audio": true,
  "output_audio_type": "speech",
  "characteristics": "{\"processType\": \"synthesis\", \"audioGeneration\": \"text-to-speech\", \"lipSync\": true, \"motionType\": \"facial\"}",
  "metadata_maturity_level": "mature",
  "metadata_common_use_cases": ["Avatar creation", "Character animation from portrait"],
  "metadata_platforms": ["Replicate", "FAL AI", "HeyGen"],
  "metadata_example_models": ["Wav2Lip", "SadTalker", "DreamTalk"],
  "relationships": "{}",
  "output_modality": "video",
  "operation_type": "creation"
}

Note: The characteristics and relationships fields are JSON strings that should be parsed when needed.

Data Fields

JSONL record fields:

• id (string): Unique identifier in kebab-case
• name (string): Human-readable name
• input_primary (string): Main input modality
• input_secondary (list of strings): Additional optional inputs
• output_primary (string): Main output modality
• output_audio (boolean): Whether audio is included (for video outputs)
• output_audio_type (string): Type of audio (speech, music, ambient, etc.)
• characteristics (JSON string): Modality-specific features (parse with json.loads)
• metadata_maturity_level (string): experimental, emerging, or mature
• metadata_common_use_cases (list of strings): Typical use cases
• metadata_platforms (list of strings): Platforms supporting this modality
• metadata_example_models (list of strings): Example model implementations
• relationships (JSON string): Links to related modalities (parse with json.loads)
• output_modality (string): The primary output type (video, audio, image, text, 3d-model)
• operation_type (string): Either "creation" or "editing"

Data Splits

This dataset is provided as a complete reference taxonomy without splits.

Dataset Creation

Curation Rationale

The rapid development of multimodal AI has created a complex landscape with hundreds of model variants. Platforms like Replicate and FAL AI offer numerous models that differ not just in parameters or resolution, but in fundamental modality support. For example, among 20+ image-to-video models, some generate silent video, others add ambient audio, and some include lip-synced speech - but these differences aren't easily filterable.

This taxonomy addresses the need for:

1. Systematic categorization of multimodal capabilities
2. Fine-grained filtering beyond basic input/output types
3. Discovery of models matching specific use cases
4. Understanding of the multimodal AI landscape

Source Data

The taxonomy is curated from:

• Public AI model platforms (Replicate, FAL AI, HuggingFace, RunwayML, etc.)
• Research papers and model documentation
• Community knowledge and testing
• Direct platform API exploration

Annotations

All entries are manually curated and categorized based on model documentation, testing, and platform specifications.

Considerations for Using the Data

Social Impact

This dataset is designed to:

• Democratize access to understanding multimodal AI capabilities
• Enable better model selection for specific use cases
• Support research into multimodal AI trends and capabilities

Discussion of Biases

The taxonomy reflects:

• Current state of publicly accessible multimodal AI (as of 2025)
• Platform availability bias toward commercial services
• Maturity level assessments based on community adoption and stability

Other Known Limitations

• The field is rapidly evolving; new modalities emerge regularly
• Platform and model availability changes over time
• Some experimental modalities may have limited real-world implementations
• Coverage may be incomplete for niche or newly emerging modalities

Additional Information

Dataset Curators

Created and maintained as an open-source project for the multimodal AI community.

Licensing Information

Creative Commons Zero v1.0 Universal (CC0 1.0) - Public Domain Dedication

Citation Information

If you use this taxonomy in your research or projects, please cite:

@dataset{multimodal_ai_taxonomy,
  title={Multimodal AI Taxonomy},
  author={Community Contributors},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/YOUR_USERNAME/multimodal-ai-taxonomy}}
}

Contributions

This is an open-source taxonomy that welcomes community contributions. To add new modalities or update existing entries:

1. Follow the schema defined in taxonomy/schema.json
2. Add entries to the appropriate modality file based on output type and operation
3. Submit a pull request with clear documentation

For detailed contribution guidelines, see taxonomy/README.md.

Usage Examples

Loading the Dataset

from datasets import load_dataset
import json

# Load the entire taxonomy
dataset = load_dataset("danielrosehill/multimodal-ai-taxonomy", split="train")

# The dataset is now a flat structure - iterate through records
for record in dataset:
    print(f"{record['name']}: {record['output_modality']} {record['operation_type']}")

Filtering by Characteristics

import json
from datasets import load_dataset

# Load dataset
dataset = load_dataset("danielrosehill/multimodal-ai-taxonomy", split="train")

# Find all video generation modalities with lip sync
lipsync_modalities = []
for record in dataset:
    if record['output_modality'] == 'video' and record['operation_type'] == 'creation':
        characteristics = json.loads(record['characteristics'])
        if characteristics.get('lipSync'):
            lipsync_modalities.append(record)

for modality in lipsync_modalities:
    print(f"{modality['name']}: {modality['id']}")

Finding Models by Use Case

from datasets import load_dataset

# Load dataset
dataset = load_dataset("danielrosehill/multimodal-ai-taxonomy", split="train")

# Find mature image generation methods
mature_image_gen = [
    record for record in dataset
    if record['output_modality'] == 'image'
    and record['operation_type'] == 'creation'
    and record['metadata_maturity_level'] == 'mature'
]

for method in mature_image_gen:
    print(f"{method['name']}")
    print(f"  Platforms: {', '.join(method['metadata_platforms'])}")
    print(f"  Models: {', '.join(method['metadata_example_models'])}")

Contact

For questions, suggestions, or contributions, please open an issue in the dataset repository.