title: Cora: Correspondence-aware image editing using few step diffusion
summary: Image editing is an important task in computer graphics, vision, and VFX,
  with recent diffusion-based methods achieving fast and high-quality results.
  However, edits requiring significant structural changes, such as non-rigid
  deformations, object modifications, or content generation, remain challenging.
  Existing few step editing approaches produce artifacts such as irrelevant
  texture or struggle to preserve key attributes of the source image (e.g.,
  pose). We introduce Cora, a novel editing framework that addresses these
  limitations by introducing correspondence-aware noise correction and
  interpolated attention maps. Our method aligns textures and structures between
  the source and target images through semantic correspondence, enabling accurate
  texture transfer while generating new content when necessary. Cora offers
  control over the balance between content generation and preservation. Extensive
  experiments demonstrate that, quantitatively and qualitatively, Cora excels in
  maintaining structure, textures, and identity across diverse edits, including
  pose changes, object addition, and texture refinements. User studies confirm
  that Cora delivers superior results, outperforming alternatives.
paper_authors._id: 683e6838a6815c77acb18ea3
paper_authors.name: Amirhossein Almohammadi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:45:59.757Z
paper_authors._id: 650e3abcae507a2c7c847baa
paper_authors.avatarUrl: /avatars/a138bc6c9ba80a486fb36f4e1aa16b33.svg
paper_authors.fullname: Amirhossein Alimohammadi
paper_authors.type: user
paper_authors.user: Amirhossein-Alimohammadi
paper_authors._id: 683e6838a6815c77acb18ea4
paper_authors.name: Aryan Mikaeili
paper_authors._id: 683e6838a6815c77acb18ea5
paper_authors.name: Sauradip Nag
paper_authors._id: 683e6838a6815c77acb18ea6
paper_authors.name: Negar Hassanpour
paper_authors._id: 683e6838a6815c77acb18ea7
paper_authors.name: Andrea Tagliasacchi
paper_authors._id: 683e6838a6815c77acb18ea8
paper_authors.name: Ali Mahdavi-Amiri
paper_title: Cora: Correspondence-aware image editing using few step diffusion
paper_summary: Image editing is an important task in computer graphics, vision, and VFX,
  with recent diffusion-based methods achieving fast and high-quality results.
  However, edits requiring significant structural changes, such as non-rigid
  deformations, object modifications, or content generation, remain challenging.
  Existing few step editing approaches produce artifacts such as irrelevant
  texture or struggle to preserve key attributes of the source image (e.g.,
  pose). We introduce Cora, a novel editing framework that addresses these
  limitations by introducing correspondence-aware noise correction and
  interpolated attention maps. Our method aligns textures and structures between
  the source and target images through semantic correspondence, enabling accurate
  texture transfer while generating new content when necessary. Cora offers
  control over the balance between content generation and preservation. Extensive
  experiments demonstrate that, quantitatively and qualitatively, Cora excels in
  maintaining structure, textures, and identity across diverse edits, including
  pose changes, object addition, and texture refinements. User studies confirm
  that Cora delivers superior results, outperforming alternatives.
paper_ai_summary: Cora framework enhances image editing through correspondence-aware noise correction and interpolated attention maps, excelling in structure and texture preservation and generation.
paper_ai_keywords: diffusion-based methods non-rigid deformations object modifications content generation correspondence-aware noise correction interpolated attention maps semantic correspondence texture transfer content generation preservation pose changes object addition texture refinements