Cora: Correspondence-aware image editing using few step diffusion
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.
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Amirhossein Almohammadi
claimed_verified
2025-06-03T08:45:59.757Z
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Amirhossein Alimohammadi
user
Amirhossein-Alimohammadi
683e6838a6815c77acb18ea4
Aryan Mikaeili
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Sauradip Nag
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Negar Hassanpour
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Andrea Tagliasacchi
683e6838a6815c77acb18ea8
Ali Mahdavi-Amiri
Cora: Correspondence-aware image editing using few step diffusion
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.
Cora framework enhances image editing through correspondence-aware noise correction and interpolated attention maps, excelling in structure and texture preservation and generation.
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