OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing
[Haoyang He1*](https://scholar.google.com/citations?hl=zh-CN&user=8NfQv1sAAAAJ),
Jie Wang2*,
[Jiangning Zhang1#](https://zhangzjn.github.io),
[Zhucun Xue1](https://scholar.google.com/citations?user=m3KDreEAAAAJ&hl=en),
[Xingyuan Bu2](https://scholar.google.com/citations?hl=en&user=cqYaRhUAAAAJ&view_op=list_works),
[Qiangpeng Yang2](https://scholar.google.com/citations?user=vr9z1VQAAAAJ&hl=en&oi=ao),
[Shilei Wen2](https://scholar.google.com/citations?user=zKtYrHYAAAAJ&hl=en&oi=ao),
[Lei Xie1#](https://scholar.google.com/citations?hl=zh-CN&user=7ZZ_-m0AAAAJ),
1Zhejiang University, 2Bytedance
\*Equal Contribution. \# Corresponding Author.
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## 📑 Open-Source Plan
The dataset, code, model, and benchmark are currently under review. Please stay tuned.
- [x] OpenVE-3M Dataset
- [ ] OpenVE-Edit Model
- [x] OpenVE-Bench Benchmark
- [ ] Inference & Multi-gpus Sequence Parallel inference
- [ ] Fine-tuning & Lora-tuning scripts
## 🌍 Introduction
The quality and diversity of instruction-based image editing datasets are continuously increasing, yet large-scale, high-quality datasets for instruction-based video editing remain scarce. To address this gap, we introduce OpenVE-3M, an open-source, large-scale, and high-quality dataset for instruction-based video editing. It comprises two primary categories: spatially-aligned edits (Global Style, Background Change, Local Change, Local Remove, Local Add, and Subtitles Edit) and non-spatially-aligned edits (Camera Multi-Shot Edit and Creative Edit). All edit types are generated via a meticulously designed data pipeline with rigorous quality filtering. OpenVE-3M surpasses existing open-source datasets in terms of scale, diversity of edit types, instruction length, and overall quality. Furthermore, to address the lack of a unified benchmark in the field, we construct OpenVE-Bench, containing 431 video-edit pairs that cover a diverse range of editing tasks with three key metrics highly aligned with human judgment. We present OpenVE-Edit, a 5B model trained on our dataset that demonstrates remarkable efficiency and effectiveness by setting a new state-of-the-art on OpenVE-Bench, outperforming all prior open-source models including a 14B baseline.
Demonstration of Eight different categories on the same video from the proposed OpenVE-3M dataset.
## 🔗 Citation
If you find OpenVE useful for your research and applications, please cite using this BibTeX:
```
@article{he2025openve-3m,
title={OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing},
author={Haoyang He, Jie Wang, Jiangning Zhang, Zhucun Xue, Xingyuan Bu, Qiangpeng Yang, Shilei Wen, Lei Xie},
journal={arXiv preprint arXiv:2512.07826},
year={2025}
}
```
