Summary of Re-attentional Controllable Video Diffusion Editing, by Yuanzhi Wang et al.
Re-Attentional Controllable Video Diffusion Editing
by Yuanzhi Wang, Yong Li, Mengyi Liu, Xiaoya Zhang, Xin Liu, Zhen Cui, Antoni B. Chan
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a novel method, Re-Attentional Controllable Video Diffusion Editing (ReAtCo), to overcome limitations in text-guided video editing. Recent studies have leveraged large-scale text-to-image diffusion models for this task, but they may still suffer from mislocated objects and incorrect object counts. To address these issues, the proposed ReAtCo method uses a Re-Attentional Diffusion (RAD) mechanism to refocus cross-attention responses between the edited text prompt and target video during denoising. This allows for spatially location-aligned and semantically high-fidelity manipulated videos. Additionally, an Invariant Region-guided Joint Sampling (IRJS) strategy is proposed to mitigate intrinsic sampling errors and preserve invariant region content with minimal border artifacts. Experimental results demonstrate that ReAtCo improves the controllability of video diffusion editing and achieves superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to edit videos using text has been developed, making it easier and more accurate. Current methods can still make mistakes, such as placing objects in the wrong location or not including enough objects. To fix this, researchers have created a new method that uses attention to focus on the right parts of the video when editing. This helps ensure that the edited video looks correct and makes sense. The method also preserves important details, like unchanging backgrounds, with minimal errors. Overall, this new approach improves video editing and makes it more reliable. |
Keywords
» Artificial intelligence » Attention » Cross attention » Diffusion » Prompt
