基于扩散模型的一致性图像布局编辑
Consistent Image Layout Editing with Diffusion Models
AI总结：
本文提出一种基于扩散模型的图像布局编辑方法，通过多概念学习和中间特征语义一致性投影，在重新排列图像布局的同时保持物体外观一致，实验证明其在布局对齐和视觉一致性上优于现有方法。
摘要：
尽管大规模文本到图像扩散模型在图像生成和图像编辑方面取得了巨大成功，现有方法在编辑真实图像的布局方面仍然面临困难。虽然已有少数工作尝试解决这一问题，但它们要么无法调整图像布局，要么在布局调整后难以保持物体的视觉外观。为了弥补这一差距，本文提出了一种新颖的图像布局编辑方法，该方法不仅能够将真实图像重新排列到指定布局，还能确保物体的视觉外观与编辑前保持一致。具体而言，所提方法包含两个关键组成部分。首先，采用多概念学习方案从单张图像中学习不同物体的概念，这对于在布局编辑中保持视觉一致性至关重要。其次，利用扩散模型中间特征内部的语义一致性，将物体的外观信息直接投影到目标区域。此外，还采用了一种新颖的初始化噪声设计，以促进布局重新排列的过程。大量实验表明，在图像布局编辑任务中，所提方法在布局对齐和视觉一致性方面均优于先前工作。
英文摘要：
Despite the great success of large-scale text-to-image diffusion models in image generation and image editing, existing methods still struggle to edit the layout of real images. Although a few works have been proposed to tackle this problem, they either fail to adjust the layout of images, or have difficulty in preserving visual appearance of objects after the layout adjustment. To bridge this gap, this paper proposes a novel image layout editing method that can not only re-arrange a real image to a specified layout, but also can ensure the visual appearance of the objects consistent with their appearance before editing. Concretely, the proposed method consists of two key components. Firstly, a multi-concept learning scheme is used to learn the concepts of different objects from a single image, which is crucial for keeping visual consistency in the layout editing. Secondly, it leverages the semantic consistency within intermediate features of diffusion models to project the appearance information of objects to the desired regions directly. Besides, a novel initialization noise design is adopted to facilitate the process of re-arranging the layout. Extensive experiments demonstrate that the proposed method outperforms previous works in both layout alignment and visual consistency for the task of image layout editing
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
