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arXiv · 2502.06734

Se\~norita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists

Bojia Zi·Penghui Ruan·Marco Chen·Xianbiao Qi·Shaozhe Hao·Shihao Zhao·Youze Huang·Bin Liang·Rong Xiao·Kam-Fai Wong
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Abstract

Recent advancements in video generation have spurred the development of video editing techniques, which can be divided into inversion-based and end-to-end methods. However, current video editing methods still suffer from several challenges. Inversion-based methods, though training-free and flexible, are time-consuming during inference, struggle with fine-grained editing instructions, and produce artifacts and jitter. On the other hand, end-to-end methods, which rely on edited video pairs for training, offer faster inference speeds but often produce poor editing results due to a lack of high-quality training video pairs. In this paper, to close the gap in end-to-end methods, we introduce Se\~norita-2M, a high-quality video editing dataset. Se\~norita-2M consists of approximately 2 millions of video editing pairs. It is built by crafting four high-quality, specialized video editing models, each crafted and trained by our team to achieve state-of-the-art editing results. We also propose a filtering pipeline to eliminate poorly edited video pairs. Furthermore, we explore common video editing architectures to identify the most effective structure based on current pre-trained generative model. Extensive experiments show that our dataset can help to yield remarkably high-quality video editing results. More details are available at https://senorita-2m-dataset.github.io.

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Bojia Zi, Penghui Ruan, Marco Chen, Xianbiao Qi, Shaozhe Hao, Shihao Zhao, Youze Huang, Bin Liang, Rong Xiao, Kam-Fai Wong. 2025-02-10. Se\~norita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists. https://arxiv.org/abs/2502.06734

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