Cora：基于少步扩散的对应感知图像编辑

摘要

图像编辑是计算机图形学、视觉和视觉特效领域的重要任务，近期基于扩散模型的方法已能实现快速且高质量的编辑效果。然而，对于需要显著结构变化的编辑任务，如非刚性变形、对象修改或内容生成，仍面临挑战。现有的少步骤编辑方法常产生无关纹理或难以保留源图像的关键属性（如姿态）。我们提出了Cora，一种新颖的编辑框架，通过引入对应感知的噪声校正和插值注意力图来解决这些局限。Cora通过语义对应关系对齐源图像与目标图像的纹理和结构，在必要时生成新内容的同时实现精确的纹理转移。Cora提供了在内容生成与保留之间平衡的控制能力。大量实验表明，无论是定量还是定性分析，Cora在保持结构、纹理和身份一致性方面均表现出色，适用于姿态变化、对象添加和纹理优化等多种编辑场景。用户研究证实，Cora提供的编辑效果优于现有方法。

English

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：基于少步扩散的对应感知图像编辑

Cora: Correspondence-aware image editing using few step diffusion

摘要

Summary

Support

Support