Summary of Click2mask: Local Editing with Dynamic Mask Generation, by Omer Regev et al.
Click2Mask: Local Editing with Dynamic Mask Generation
by Omer Regev, Omri Avrahami, Dani Lischinski
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); 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 paper proposes a novel approach called Click2Mask for local image editing, which simplifies the process by requiring only a single point of reference. The method uses Blended Latent Diffusion (BLD) to dynamically grow a mask around this point, guided by a masked CLIP-based semantic loss. This approach surpasses existing methods that require precise masks or detailed descriptions, offering a more user-friendly and contextually accurate solution. The paper demonstrates the effectiveness of Click2Mask through experiments that show it minimizes user effort while enabling competitive or superior local image manipulations compared to state-of-the-art (SoTA) methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Local image editing gets easier with Click2Mask! This new method makes it simple to add new content to a specific area without needing a precise mask or detailed description. Just point to where you want to make the change, and the system will figure out the rest using a special kind of AI called Blended Latent Diffusion (BLD). The result is an image that’s been edited with more accuracy and less hassle. |
Keywords
» Artificial intelligence » Diffusion » Mask
