SimGraph：基于场景图的图像生成与编辑统一框架
SimGraph: A Unified Framework for Scene Graph-Based Image Generation and Editing
AI总结：
SimGraph通过整合基于场景图的生成与编辑，实现了对物体关系和空间布局的精准控制，提升图像生成与编辑的一致性和质量。
摘要：
近期生成式人工智能（GenAI）的发展显著增强了图像生成和编辑的能力。然而，当前方法往往将这些任务分开处理，导致效率低下，并在保持生成内容与编辑之间空间一致性和语义连贯性方面面临挑战。此外，一个主要障碍是缺乏对物体关系和空间布局的结构化控制。基于场景图的方法通过以结构化格式表示物体及其相互关系，为图像生成和编辑提供了更大的控制能力。为此，我们引入了SimGraph，一个统一的框架，整合了基于场景图的图像生成和编辑，使能够精确控制物体互动、布局和空间一致性。特别是，我们的框架在单一场景图驱动模型中集成了基于标记的生成和扩散-based编辑，确保高质量和一致的结果。通过广泛的实验，我们实证证明我们的方法优于现有最先进方法。
英文摘要：
Recent advancements in Generative Artificial Intelligence (GenAI) have significantly enhanced the capabilities of both image generation and editing. However, current approaches often treat these tasks separately, leading to inefficiencies and challenges in maintaining spatial consistency and semantic coherence between generated content and edits. Moreover, a major obstacle is the lack of structured control over object relationships and spatial arrangements. Scene graph-based methods, which represent objects and their interrelationships in a structured format, offer a solution by providing greater control over composition and interactions in both image generation and editing. To address this, we introduce SimGraph, a unified framework that integrates scene graph-based image generation and editing, enabling precise control over object interactions, layouts, and spatial coherence. In particular, our framework integrates token-based generation and diffusion-based editing within a single scene graph-driven model, ensuring high-quality and consistent results. Through extensive experiments, we empirically demonstrate that our approach outperforms existing state-of-the-art methods.
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。
