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

Understand

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.