Summary of Videoshop: Localized Semantic Video Editing with Noise-extrapolated Diffusion Inversion, by Xiang Fan et al.
Videoshop: Localized Semantic Video Editing with Noise-Extrapolated Diffusion Inversion
by Xiang Fan, Anand Bhattad, Ranjay Krishna
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed Videoshop algorithm enables users to make localized semantic edits to videos without requiring any additional training data or specific editing software knowledge. By allowing users to modify the first frame and then automatically propagating those changes to the remaining frames, Videoshop offers fine-grained control over object addition, removal, and modification, as well as inserting stock photos into videos. This is achieved through image-based video editing by inverting latents with noise extrapolation, generating videos conditioned on the edited image. The algorithm outperforms six baselines on two editing benchmarks using ten evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Videoshop is a new way to edit videos without needing special training or software. It lets you change just one frame and then makes the same changes to all the other frames in the video, so it looks like the objects moved naturally. You can add or remove things, change what’s there, or even add photos from your computer. This is possible because Videoshop uses a special kind of editing that starts with an image and then generates a video based on that image. |
