Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists

• URL: http://arxiv.org/abs/2502.06734v3
• Date: Wed, 12 Mar 2025 07:47:48 GMT
• Title: Señorita-2M: A High-Quality Instruction-based Dataset for General Video Editing by Video Specialists
• Authors: Bojia Zi, Penghui Ruan, Marco Chen, Xianbiao Qi, Shaozhe Hao, Shihao Zhao, Youze Huang, Bin Liang, Rong Xiao, Kam-Fai Wong,
• Abstract summary: We introduce Senorita-2M, a high-quality video editing dataset.<n>It is built by crafting four high-quality, specialized video editing models.<n>We propose a filtering pipeline to eliminate poorly edited video pairs.
• Score: 17.451911831989293
• License: http://creativecommons.org/licenses/by/4.0/
• 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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