title: Pathways on the Image Manifold: Image Editing via Video Generation
summary: 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 state-of-the-art results on text-based image editing,
  demonstrating significant improvements in both edit accuracy and image
  preservation.
paper_authors._id: 6746c37efb7a1de1dca06a6d
paper_authors.name: Noam Rotstein
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-27T22:19:56.306Z
paper_authors._id: 62b3e85bcbd2a402fc7804b1
paper_authors.avatarUrl: /avatars/63125ce8a1e20b8c6e836f223d24284f.svg
paper_authors.fullname: noam rotstein
paper_authors.type: user
paper_authors.user: noamrot
paper_authors._id: 6746c37efb7a1de1dca06a6e
paper_authors.name: Gal Yona
paper_authors._id: 6746c37efb7a1de1dca06a6f
paper_authors.name: Daniel Silver
paper_authors._id: 6746c37efb7a1de1dca06a70
paper_authors.name: Roy Velich
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-29T10:08:14.111Z
paper_authors._id: 63ecaf7460ff4b318ad03ebb
paper_authors.avatarUrl: /avatars/7a5bf1854f1eae9cc5fd8392a3f9fba3.svg
paper_authors.fullname: Roy Velich
paper_authors.type: user
paper_authors.user: royve
paper_authors._id: 6746c37efb7a1de1dca06a71
paper_authors.name: David Bensaïd
paper_authors._id: 6746c37efb7a1de1dca06a72
paper_authors.name: Ron Kimmel
paper_title: Pathways on the Image Manifold: Image Editing via Video Generation
paper_summary: 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 state-of-the-art results on text-based image editing,
  demonstrating significant improvements in both edit accuracy and image
  preservation.
paper_ai_keywords: image diffusion models image-to-video models temporal process pretrained video models image manifold text-based image editing edit accuracy image preservation