MEV: A Multi-Event Video Dataset for Long-Take Generation

       

MEV is a dataset for long-take multi-event video generation, with 8k real-world videos and 25k temporally grounded events. It provides Global and Event annotations, including multi-granularity captions, camera descriptions, semantic attributes, and six VBench quality scores.

The canonical nested annotation file is annotations/mev.json. The primary machine-readable table is metadata/events.parquet, where each row corresponds to one event segment.

Repository Layout

• annotations/mev.json: canonical full annotation file.
• metadata/events.parquet: full event-level table.
• metadata/events.csv and mev.csv: CSV mirrors for inspection.
• metadata/videos.parquet: UUID-level index.
• raw/720p/raw-*.tar: 8,016 original videos, stored as <uuid>.mp4.
• raw/1080p/raw-*.tar: 1,896 optional higher-resolution originals.
• videos/mev-*.tar: full video segment tar shards. Tar members preserve the original mp4 filenames.
• samples/: reviewer-friendly sample subset with matching annotation, metadata, and tar shard(s).

Loading Metadata

from datasets import load_dataset

events = load_dataset("MultiEventVideo/MEV", split="events")
videos = load_dataset("MultiEventVideo/MEV", "videos", split="videos")
sample_events = load_dataset("MultiEventVideo/MEV", split="sample_events")

The default events configuration contains the events and sample_events splits. The videos configuration contains videos and sample_videos. These are table and inspection-subset names, not experimental train/test splits.

Each event row includes shard_path, tar_member, and media_ref. For example:

videos/mev-000000.tar::<original-segment-filename>.mp4

has_cooking is a boolean indicating food or beverage preparation, or active cooking/heating, during at least part of the event.

Download and Extract Videos

Loading the metadata with load_dataset does not download the video archives. The following commands use a macOS or Linux shell.

Download the dataset

Install the Hugging Face CLI and download the repository to a local MEV directory:

python -m pip install -U huggingface_hub
hf download MultiEventVideo/MEV --repo-type dataset --local-dir ./MEV
cd MEV

The complete download is approximately 227 GB. Run the commands below from this MEV directory. Individual archives can also be downloaded from the repository's Files tab, preserving the same directory layout.

Extract the original and event videos

The .tar archives contain MP4 files and have no additional gzip compression. Create an extracted/ subdirectory within each archive directory and extract the videos there. The tar files remain in their existing locations:

for directory in raw/720p raw/1080p videos; do
  mkdir -p "$directory/extracted"
  for archive in "$directory"/*.tar; do
    tar -xf "$archive" -C "$directory/extracted"
  done
done

After running these commands, the directories contain:

raw/720p/
  raw-*.tar
  extracted/<uuid>.mp4                  # 8,016 primary original videos
raw/1080p/
  raw-*.tar
  extracted/<uuid>.mp4                  # 1,896 available alternate originals
videos/
  mev-*.tar
  extracted/<original-event-filename>.mp4 # 25,262 event videos

Here, raw/ holds original videos, while videos/ holds Event videos.

Open the extracted MP4 files in a video player. Original filenames are preserved: match raw videos by uuid and event videos by each annotation's filename. The 1080p directory contains alternate originals, not additional dataset UUIDs. Keeping the archives and all extracted full-data videos uses approximately 450 GB.

To inspect a single archive before extracting it:

tar -tf videos/mev-000000.tar

Videos can also be read directly from their tar archives using the Python example under Original video access, without extracting the complete archives.

Sample Subset

The samples/ directory contains a smaller subset intended for reviewer inspection. It is drawn from the full MEV release at the UUID level; all events from selected UUIDs are preserved.

Selection includes all videos with saved human-review records from the web annotation interface that are present in this release: 226 original videos and 721 events. Annotations are copied from the current full dataset. Selection is at the video level; it does not imply that every field was individually reviewed. samples/uuids.txt records the exact membership. samples/raw/720p/ contains the original videos, and samples/videos/ contains their event videos. This is an inspection subset, not a random population sample or the experimental train/test split.

License

This repository uses mixed-third-party-licenses.

Original Mixkit, YouTube, and other third-party videos remain subject to their respective platform terms and rights-holder licenses. The dataset authors do not claim ownership of third-party video assets and do not re-license them under CC BY, MIT, Apache, or any other open-source license.

The official released video package is directly downloadable for research, evaluation, and model training, subject to applicable source-specific terms described in LICENSE. Optional restricted or premium source variants are not automatically part of the public package. Preserve available source attribution and provenance. The repository's existing LICENSE is authoritative.

Annotations, labels, splits, benchmark metadata, and other author-created materials are released under the Creative Commons Attribution 4.0 International License, CC BY 4.0.

See the LICENSE file for full details.

Original video access

The dataset contains 8,016 original videos and 25,262 events.

The Global table (metadata/videos.parquet) includes raw_720p_shard_path, raw_720p_tar_member, raw_720p_media_ref, raw_720p_bytes, and raw_720p_sha256. The full table also links available alternate originals with the corresponding raw_1080p_* columns. Empty alternative references indicate that the table does not provide that variant. The sample supplies the primary original videos. Resolution directory names preserve the source storage groups; actual media dimensions can vary. Every tar preserves the original MP4 bytes and filenames.

All archives are uncompressed tar shards, each at most 5 GiB. To read an original video and its first event directly from the downloaded archives, install the table-reading dependencies:

python -m pip install pandas pyarrow

Then run this Python example from the downloaded MEV directory:

from pathlib import Path
import tarfile
import pandas as pd

root = Path(".")
videos = pd.read_parquet(root / "metadata/videos.parquet")
events = pd.read_parquet(root / "metadata/events.parquet")
video = videos.iloc[0]
event = events[events.uuid == video.uuid].iloc[0]

def read_media(reference):
    archive, member = reference.split("::", 1)
    with tarfile.open(root / archive) as tar:
        return tar.extractfile(member).read()

raw_bytes = read_media(video.raw_720p_media_ref)
event_bytes = read_media(event.media_ref)

Join the Global and Event tables on uuid. Each Event row stores its original video timestamps, an event ID starting at 1, its tar reference, byte size, and SHA-256. The HF table split names identify data views, not experimental train/test assignments.

Citation

If you use MEV in your research, please cite:

@misc{mev2026,
  title  = {MEV: A Multi-Event Video Dataset for Long-Take Generation},
  author = {Zhu, Peiyuan and Xie, Shaoan and Shen, Yifan and Tian, Wen and
            Li, Zijian and Chen, Guangyi and Zhang, Kun},
  year   = {2026},
  url    = {https://mev-dataset.github.io/}
}