Summary of Anyv2v: a Tuning-free Framework For Any Video-to-video Editing Tasks, by Max Ku and Cong Wei and Weiming Ren and Harry Yang and Wenhu Chen
AnyV2V: A Tuning-Free Framework For Any Video-to-Video Editing Tasks
by Max Ku, Cong Wei, Weiming Ren, Harry Yang, Wenhu Chen
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers tackle the challenge of achieving state-of-the-art quality and control in digital content creation using generative models for video editing. They introduce AnyV2V, a novel tuning-free paradigm that simplifies video editing into two steps: modifying the first frame with an off-the-shelf image editing model and generating the edited video through temporal feature injection using an existing image-to-video generation model. This approach allows for leveraging any existing image editing tools to support various video editing tasks, including prompt-based editing, reference-based style transfer, subject-driven editing, and identity manipulation. AnyV2V outperforms baseline methods in human evaluations, demonstrating improvements in visual consistency with the source video. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video editing models need to improve their quality and control. Researchers tried extending image-based generative models, but this didn’t work well. They also needed a lot of fine-tuning to get good results. Most methods used text to guide the editing, which caused problems. A new method called AnyV2V can make video editing easier by breaking it down into two steps: changing the first frame and then making the rest of the video match that frame. This works for lots of different kinds of edits and for videos of any length. People liked the results better than other methods. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Style transfer
