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

TL;DR

OpenVE-3M introduces a comprehensive, large-scale dataset for instruction-guided video editing, enabling improved model training and benchmarking in this emerging field.

Contribution

We created OpenVE-3M, the first large-scale, high-quality dataset for instruction-based video editing, and established OpenVE-Bench for standardized evaluation.

Findings

OpenVE-3M surpasses existing datasets in scale and diversity.

OpenVE-Edit model achieves state-of-the-art results on OpenVE-Bench.

Our dataset and benchmark facilitate future research in instruction-guided video editing.

Abstract

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. Project page is at https://lewandofskee.github.io/projects/OpenVE.