Summary of Diffusion Model-based Video Editing: a Survey, by Wenhao Sun et al.
Diffusion Model-Based Video Editing: A Survey
by Wenhao Sun, Rong-Cheng Tu, Jingyi Liao, Dacheng Tao
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The rapid development of diffusion models (DMs) has significantly advanced image and video applications, making “what you want is what you see” a reality. The paper reviews diffusion model-based video editing techniques, including theoretical foundations and practical applications. It begins by overviewing the mathematical formulation and key methods in the image domain. The review categorizes video editing approaches based on their core technologies, depicting an evolutionary trajectory. Novel applications include point-based editing and pose-guided human video editing. A comprehensive comparison is presented using the newly introduced V2VBench. Challenges and potential directions for future research are also discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how machines can edit videos using diffusion models. This technology has improved a lot, making it easy to get what you want. The paper explains how this works and looks at different ways to do video editing. It even shows some new ideas like editing just specific points in a video or editing people’s poses. There’s also a comparison of these methods. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model
