OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing

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🌍 Overview

We introduce OpenVE-3M, an open-source, large-scale, and high-quality dataset for instruction-based video editing. The OpenVE-3M dataset includes eight major video editing categories: six SA (Global Style Transfer, Background Change, Local Change, Local Remove, Local Add, Subtitles Edit) and two NSA (Multi-Shot Camera Edit, Creative Edit). SA edits maintain perfect consistency in motion and detail between the original and edited videos. In contrast, NSA edits maintain the primary subject’s consistency but introduce new, creative motion.

Dataset Statistics

• Total Examples: 3,000,000+ video editing triplets
• Video Resolution: 720P resolutions (1280*720 / 720*1280)
• Video Length: 65-129 frames per video
• Categories: 8 Categories with 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)
• Average Instructions Length: Average 40.6 words per video

Dataset Structure

The dataset is organized as follows:

├── OpenVE-3M                    
│   ├── videos     
|       |── global_style.tar.gz.00
|       |── ...     
|       |── background_change..tar.gz.00   
|       |── ...     
|       |── local_change..tar.gz.00   
|       |── ...     
|       |── local_remove..tar.gz.00   
|       |── ...     
|       |── local_add..tar.gz.00   
|       |── ...     
|       |── subtitles_edit..tar.gz.00   
|       |── ...                    
│   ├── csv_files         
|       |── global_style.csv
|       |── background_change.csv   
|       |── local_change.csv   
|       |── local_remove.csv   
|       |── local_add.csv   
|       |── subtitles_edit.csv   
|       |── camera_edit.csv   
|       |── creative_edit.csv                        

Dataset Space Usage

• Global Style (~510G)
• Background Change (~170G)
• Local Change (~179G)
• Local Remove (~453G)
• Local Add (~760G)
• Subtitles Edit (~164G)
• Multi-Shot Camera Edit (~981G)
• Creative Edit (~1.6T)

Training CSV Files

Each csv file contains triplet items of:

• video: Path to the corresponding edited video
• original_video: Path to the source video
• prompt: Editing instruction

Downloading and Extracting the Dataset

Full Dataset Download

from datasets import load_dataset
# Download the entire dataset
dataset = load_dataset("Lewandofski/OpenVE-3M")

Extracting the Video Data

On Linux/macOS or Windows (with Git Bash/WSL):

# Navigate to the directory containing the split files
cd path/to/your/dataset/part
# For example, to extract the global_style videos:
cat global_style.tar.gz.* | tar -zxv

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}
}

LICENSE

OpenVE-3M is licensed under the CC-BY-NC-4.0 License.