Summary of Clipdrag: Combining Text-based and Drag-based Instructions For Image Editing, by Ziqi Jiang et al.
CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing
by Ziqi Jiang, Zhen Wang, Long Chen
First submitted to arxiv on: 4 Oct 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 proposes CLIPDrag, a novel image editing method that combines text and drag signals to achieve precise and ambiguity-free manipulations on diffusion models. The method addresses the drawbacks of current approaches, including imprecise text-based editing and ambiguous drag-based editing. To leverage both signals, the authors treat text as global guidance and drag points as local information, integrating text signals into existing drag-based methods using a pre-trained language-vision model like CLIP. Additionally, they present a fast point-tracking method to enforce drag points moving toward correct directions. Extensive experiments demonstrate that CLIPDrag outperforms existing single drag-based methods or text-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re editing a picture, but you want to make sure it’s just right. Most image editing methods can be grouped into two types: global and local. Global methods try to change the entire picture at once, while local methods focus on specific parts of the picture. But these methods have their own problems. Global methods can’t always get what you want, and local methods can be confusing. To fix this, researchers developed CLIPDrag, a new way to edit pictures that uses both text and drag signals to make sure changes are precise and accurate. This method combines the best of both worlds by treating text as guidance and drag points as details. It also includes a way to speed up the editing process by keeping track of where you’re dragging the picture. |
Keywords
» Artificial intelligence » Diffusion » Tracking
