Summary of Pathways on the Image Manifold: Image Editing Via Video Generation, by Noam Rotstein et al.
Pathways on the Image Manifold: Image Editing via Video Generation
by Noam Rotstein, Gal Yona, Daniel Silver, Roy Velich, David Bensaïd, Ron Kimmel
First submitted to arxiv on: 25 Nov 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 A medium-difficulty summary of the abstract follows. Recent advances in image editing have been driven by image diffusion models, which have shown remarkable progress. However, significant challenges remain as these models often struggle to accurately follow complex edit instructions and frequently compromise fidelity by altering key elements of the original image. To address this, we propose merging image editing with video generation using image-to-video models for image editing. We reformulate image editing as a temporal process, utilizing pretrained video models to create smooth transitions from the original image to the desired edit. This approach traverses the image manifold continuously, ensuring consistent edits while preserving the original image’s key aspects. Our proposed approach achieves state-of-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image editing has made progress thanks to image diffusion models, but there are still challenges. These models can’t follow complex instructions well and often change important parts of the original image. To fix this, we’re combining image editing with video generation using special models that create smooth changes from the original image to the desired edit. This helps keep the original image’s key features while making accurate edits. |
Keywords
» Artificial intelligence » Diffusion
