AVGen-Bench Generated Videos Data Card

Overview

This data card describes the generated audio-video outputs stored directly in the repository root by model directory.

The collection is intended for benchmarking and qualitative/quantitative evaluation of text-to-audio-video (T2AV) systems. It is not a training dataset. Each item is a model-generated video produced from a prompt defined in prompts/*.json.

For Hugging Face Hub compatibility, the repository includes a root-level metadata.parquet file so the Dataset Viewer can expose each video as a structured row with prompt metadata instead of treating the repo as an unindexed file dump. The relative video path is stored as a plain string column (video_path) rather than a media-typed file_name column, which avoids current Dataset Viewer post-processing failures on video rows.

What This Dataset Contains

The dataset is organized by:

1. Model directory
2. Video category
3. Generated .mp4 files

A typical top-level structure is:

AVGen-Bench/
├── Kling_2.6/
├── LTX-2/
├── LTX-2.3/
├── MOVA_360p_Emu3.5/
├── MOVA_360p_NanoBanana_2/
├── Ovi_11/
├── Seedance_1.5_pro/
├── Sora_2/
├── Veo_3.1_fast/
├── Veo_3.1_quality/
├── Wan_2.2_HunyuanVideo-Foley/
├── Wan_2.6/
├── metadata.parquet
├── prompts/
└── reference_image/   # optional, depending on generation pipeline

Within each model directory, videos are grouped by category, for example:

Veo_3.1_fast/
├── ads/
├── animals/
├── asmr/
├── chemical_reaction/      
├── cooking/
├── gameplays/
├── movie_trailer/
├── musical_instrument_tutorial/
├── news/
├── physical_experiment/
└── sports/

Prompt Coverage

Prompt definitions are stored in prompts/*.json.

The current prompt set contains 235 prompts across 11 categories:

Category	Prompt count
ads	20
animals	20
asmr	20
chemical_reaction	20
cooking	20
gameplays	20
movie_trailer	20
musical_instrument_tutorial	35
news	20
physical_experiment	20
sports	20

Prompt JSON entries typically contain:

• content: a short content descriptor used for naming or indexing
• prompt: the full generation prompt

Data Instance Format

Each generated item is typically:

• A single .mp4 file
• Containing model-generated video and, when supported by the model/pipeline, synthesized audio
• Stored under <model>/<category>/

The filename is usually derived from prompt content after sanitization. Exact naming may vary by generation script or provider wrapper. In the standard export pipeline, the filename is derived from the prompt's content field using the following logic:

def safe_filename(name: str, max_len: int = 180) -> str:
    name = str(name).strip()
    name = re.sub(r"[/\\:*?\"<>|\\n\\r\\t]", "_", name)
    name = re.sub(r"\\s+", " ", name).strip()
    if not name:
        name = "untitled"
    if len(name) > max_len:
        name = name[:max_len].rstrip()
    return name

So the expected output path pattern is:

<model>/<category>/<safe_filename(content)>.mp4

For Dataset Viewer indexing, metadata.parquet stores one row per exported video with:

• video_path: relative path to the .mp4 stored as a plain string
• model: model directory name
• category: benchmark category
• content: prompt short name
• prompt: full generation prompt
• prompt_id: index inside prompts/<category>.json

How The Data Was Produced

The videos were generated by running different T2AV systems on a shared benchmark prompt set.

Important properties:

• All systems are evaluated against the same category structure
• Outputs are model-generated rather than human-recorded
• Different models may expose different generation settings, resolutions, or conditioning mechanisms
• Some pipelines may additionally use first-frame or reference-image inputs, depending on the underlying model

Intended Uses

This dataset is intended for:

• Benchmarking T2AV generation systems
• Running AVGen-Bench evaluation scripts
• Comparing failure modes across models
• Qualitative demo curation
• Error analysis by category or prompt type

Out-of-Scope Uses

This dataset is not intended for:

• Training a general-purpose video generation model
• Treating model outputs as factual evidence of real-world events
• Safety certification of a model without additional testing
• Any claim that benchmark performance fully captures downstream deployment quality

Known Limitations

• Outputs are synthetic and inherit the biases and failure modes of the generating models
• Some categories emphasize benchmark stress-testing rather than natural real-world frequency
• File availability may vary across models if a generation job failed, timed out, or was filtered
• Different model providers enforce different safety and moderation policies; some prompts may be rejected during provider-side review, which can lead to missing videos for specific models even when the prompt exists in the benchmark

Risks and Responsible Use

Because these are generated videos:

• Visual realism does not imply factual correctness
• Audio may contain artifacts, intelligibility failures, or misleading synchronization
• Generated content may reflect stereotypes, implausible causal structure, or unsafe outputs inherited from upstream models

Anyone redistributing results should clearly label them as synthetic model outputs.