BrushEdit：一体化图像修复与编辑
BrushEdit: All-In-One Image Inpainting and Editing
AI总结：
BrushEdit提出一种基于修复的指令引导图像编辑范式，通过智能体协作框架集成多模态大语言模型与双分支修复模型，实现自主、可交互的自由形式编辑，并在七项指标上表现优越。
摘要：
随着基于反演和基于指令的扩散模型方法的发展，图像编辑取得了显著进步。然而，当前基于反演的方法由于反演噪声的结构化特性，难以进行大幅修改（例如添加或移除物体），这阻碍了实质性变化。与此同时，基于指令的方法通常将用户限制在黑盒操作中，限制了用户直接指定编辑区域和编辑强度的交互能力。为了解决这些局限性，我们提出了BrushEdit，这是一种新颖的基于修复的指令引导图像编辑范式，它利用多模态大语言模型（MLLMs）和图像修复模型来实现自主、用户友好且可交互的自由形式指令编辑。具体而言，我们设计了一个系统，通过在智能体协作框架中集成MLLMs和双分支图像修复模型，来执行编辑类别分类、主体对象识别、掩码获取和编辑区域修复。大量实验表明，我们的框架有效地结合了MLLMs和修复模型，在包括掩码区域保留和编辑效果一致性在内的七项指标上取得了优越性能。
英文摘要：
Image editing has advanced significantly with the development of diffusion models using both inversion-based and instruction-based methods. However, current inversion-based approaches struggle with big modifications (e.g., adding or removing objects) due to the structured nature of inversion noise, which hinders substantial changes. Meanwhile, instruction-based methods often constrain users to black-box operations, limiting direct interaction for specifying editing regions and intensity. To address these limitations, we propose BrushEdit, a novel inpainting-based instruction-guided image editing paradigm, which leverages multimodal large language models (MLLMs) and image inpainting models to enable autonomous, user-friendly, and interactive free-form instruction editing. Specifically, we devise a system enabling free-form instruction editing by integrating MLLMs and a dual-branch image inpainting model in an agent-cooperative framework to perform editing category classification, main object identification, mask acquisition, and editing area inpainting. Extensive experiments show that our framework effectively combines MLLMs and inpainting models, achieving superior performance across seven metrics including mask region preservation and editing effect coherence.
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。
