Summary of Investigating the Effectiveness Of Cross-attention to Unlock Zero-shot Editing Of Text-to-video Diffusion Models, by Saman Motamed and Wouter Van Gansbeke and Luc Van Gool
Investigating the Effectiveness of Cross-Attention to Unlock Zero-Shot Editing of Text-to-Video Diffusion Models
by Saman Motamed, Wouter Van Gansbeke, Luc Van Gool
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Medium Difficulty summary: This paper explores the role of cross-attention in Text-to-Video (T2V) diffusion models for zero-shot video editing. Building on recent advances in image and video diffusion models for content creation, the authors demonstrate zero-shot control over object shape, position, and movement in T2V models, outperforming one-shot approaches. The paper highlights the potential of cross-attention guidance as a promising approach for editing videos. Specifically, it proposes manipulating the cross-attention layers of T2V diffusion models to customize their generated content, achieving improved control over object motion and temporal consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research looks at how to edit videos without any training data. Right now, we can only make small changes to videos with a little help from AI. But the authors want to see if they can make bigger changes just by using text to tell the video what to do. They’re testing a new way of doing this called cross-attention, which helps the AI understand what’s happening in the video and make better choices. The goal is to be able to edit videos without needing any training data at all. |
Keywords
» Artificial intelligence » Cross attention » One shot » Zero shot
