--- license: other license_name: mixed-third-party-licenses license_link: LICENSE configs: - config_name: events default: true data_files: - split: events path: metadata/events.parquet - split: sample_events path: samples/metadata/events.parquet - config_name: videos data_files: - split: videos path: metadata/videos.parquet - split: sample_videos path: samples/metadata/videos.parquet --- # MEV: A Multi-Event Video Dataset for Long-Take Generation

Paper · OpenReview  GitHub · Code  Project Page  Citation · BibTeX  NeurIPS 2026

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 `.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 ```python 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: ```text videos/mev-000000.tar::.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](https://huggingface.co/docs/huggingface_hub/guides/cli) and download the repository to a local `MEV` directory: ```bash 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: ```bash 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: ```text raw/720p/ raw-*.tar extracted/.mp4 # 8,016 primary original videos raw/1080p/ raw-*.tar extracted/.mp4 # 1,896 available alternate originals videos/ mev-*.tar extracted/.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: ```bash 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: ```bash python -m pip install pandas pyarrow ``` Then run this Python example from the downloaded `MEV` directory: ```python 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: ```bibtex @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/} } ```