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This dataset is curated for the Identity-Preserving Video Generation Challenge (https://hidream-ai.github.io/ipvg-challenge-2026.github.io/), which will be hosted at ACM Multimedia 2026. To request access to this dataset, please complete the registration form (https://forms.gle/j4Nwq38W9TjtPNgq9) using your Hugging Face registered email address. Access requests will be reviewed and processed within 48 hours.
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VIP-200K-Video
Overview
This repository contains only the video files (.tar archives) from the VIP-200K dataset.
For the complete dataset (including JSON annotations, face frames, and segment metadata), please download HiDream-ai/VIP-200K.
File Structure
video/
├── vip200k_train_0001_of_0100.tar
├── vip200k_train_0002_of_0100.tar
├── ...
└── vip200k_train_0100_of_0100.tar
Each .tar archive contains video clips organized by YouTube video ID:
{video_id}/
└── video/
└── {id}_{beg:05d}_{end:05d}_{fidx_beg:05d}_{fidx_end:05d}.mp4
Usage
Download and extract a specific shard:
# Download a single shard
huggingface-cli download HiDream-ai/VIP-200K-Video \
video/vip200k_train_0001_of_0100.tar \
--repo-type dataset --local-dir .
# Extract
tar -xf vip200k_train_0001_of_0100.tar
Download all video shards:
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="HiDream-ai/VIP-200K-Video",
repo_type="dataset",
local_dir="./VIP-200K-Video",
)
Complete Dataset
For annotations (JSON files with face bounding boxes, captions, segment metadata) and face frame images, please refer to the full dataset:
Citation
If you use the VIP-200K dataset or find our research helpful, please cite our paper:
@inproceedings{zhang2025identity,
title={Identity-Preserving Video Generation Challenge},
author={Zhang, Yiheng and Qiu, Zhaofan and Cai, Qi and Li, Yehao and Long, Fuchen and Pan, Yingwei and Yao, Ting and Mei, Tao},
booktitle={Proceedings of the 33rd ACM International Conference on Multimedia},
pages={13737--13742},
year={2025}
}
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