JianhuiWei/AVE_data

AVE Data

AVE Data contains generated videos and human-written weakness annotations for training and evaluating the Adaptive Video Evaluator (AVE).

Tasks and composition

Task	Train / validation / test rows	Unique generated videos	Conditioning media
Abnormality	154 / 148 / 148	353	-
Perception	60 / 60 / 63	183	-
Prompt following	39 / 39 / 39	117	165 images + 10 videos

Across the three tasks, the canonical all.json files contain 653 unique generated-video records. Abnormality has 450 split rows because 97 positive metadata records are repeated within their original split for class balancing; the video files themselves are stored once.

Download

Install the dependency and download the complete dataset:

python -m pip install "huggingface_hub>=1.0,<2"
python download.py

The public dataset is downloaded anonymously from JianhuiWei/AVE_data; no Hugging Face token or repository environment variable is needed. To download one task:

python download.py --task perception

For an immutable download, specify a tag or commit hash:

python download.py --revision COMMIT_HASH

The equivalent Hugging Face CLI command is:

hf download JianhuiWei/AVE_data \
  --repo-type dataset \
  --local-dir .

Integrity verification

The downloader verifies split JSON files, all.json, split plans, and every media file against the bundled SHA-256 metadata. Verify an existing download without network access using:

python download.py --verify-only

The AVE code repository also provides deeper schema checks:

python scripts/validate_dataset.py --data-dir data/abnormality --checksums
python scripts/validate_dataset.py --data-dir data/perception --checksums
python scripts/validate_dataset.py --data-dir data/prompt_following --checksums

Layout

AVE_data/
├── README.md
├── download.py
├── abnormality/
├── perception/
└── prompt_following/

Each task contains:

• all.json: one canonical record per generated video, without repeated IDs or generated-video paths;
• train.json, val.json, test.json: evaluator-training splits;
• SPLIT_INFO.json: split provenance and parameters;
• SPLIT_PLAN.json: exact mapping from canonical records to released split rows;
• MANIFEST.json: split and media sizes plus SHA-256 checksums;
• videos/, and conditioning-media directories where applicable.

Record schema

{
  "id": "unique record identifier",
  "prompt": "video-generation instruction",
  "reference_images": ["reference_images/example.png"],
  "reference_videos": ["reference_videos/example.mp4"],
  "video": "videos/example.mp4",
  "label": 1,
  "feedback": {
    "abnormality": "human-written weakness, or an empty string",
    "prompt_following": "",
    "consistency": ""
  }
}

reference_videos is optional for records without video conditioning. label=0 denotes an example with no annotated weakness; non-zero labels denote examples with a weakness. The human-written text in feedback.abnormality is the authoritative open-ended supervision.

Split reproducibility

The accompanying AVE code supports:

• exact reconstruction of released splits from all.json and SPLIT_PLAN.json;
• deterministic label-stratified creation of new splits;
• deterministic creation and class balancing of new splits.

Use scripts/create_splits.py in the AVE code release. The checked-in split JSON files are the canonical benchmark splits.

Intended use and limitations

The dataset supports evaluator prompt optimization, video-evaluator benchmarking, and false-positive/false-negative analysis. Human annotations may not enumerate every plausible defect. Results also depend on temporal sampling and the capabilities of the selected multimodal model. Reference videos are evaluated through sampled frames in the released implementation.

Licensing and responsibility

The software repository's MIT license applies to code. Dataset redistribution terms, media rights, attribution requirements, privacy review, and a public takedown contact should be finalized by the dataset owner before making the Hugging Face repository public.