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Cited by 14Generative Adversarial Networks and Image SynthesisDigital Media Forensic DetectionMultimodal Machine Learning Applications

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

Pathways on the Image Manifold: Image Editing via Video Generation

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Rotstein, Noam, Yona, Gal, Silver, Daniel, Velich, Roy, Bensaïd, David, Kimmel, Ron·2025·2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
DOI 10.1109/cvpr52734.2025.00736
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Abstract

Recent advances in image editing, driven by image diffusion models, have shown remarkable progress. However, significant challenges remain, as these models often struggle to follow complex edit instructions accurately and frequently compromise fidelity by altering key elements of the original image. Simultaneously, video generation has made remarkable strides, with models that effectively function as consistent and continuous world simulators. In this paper, we propose merging these two fields by utilizing image-to-video models for image editing. We reformulate image editing as a temporal process, using 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 approach achieves stateof-the-art results on text-based image editing, demonstrating significant improvements in both edit accuracy and image preservation. Visit our project page.

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Research works printed but not linked: 9

  1. [9][9] Aidan Clark, Jeff Donahue, and Karen Simonyan. Adversarial video generation on complex datasets. In ICCV, 2019. 2 -
  2. [19][19] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020. 2 -
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  4. [31][31] Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. SDEdit: Guided image synthesis and editing with stochastic differential equations. In International Conference on Learning Representations, 2022. 5 -
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Metadata

Authors
Rotstein, N., Yona, G., Silver, D., Velich, R., Bensaïd, D., & Kimmel, R.
Affiliations
Technion – Israel Institute of Technology (IL)
ORCID
Venue
2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2025-06-10
Pages
7857-7866
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr52734.2025.00736
OpenAlex
W4413146823
Cited by
5
References
0
Citations by year
2026: 5
FWCI
3.78
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
image editing, image diffusion models, image manifolds, video generation, image preservation

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