SceneCraft: 基于场景图的交互式图像编辑系统
SceneCraft: Interactive System for Image Editing via Scene Graph
AI总结：
提出SceneCraft框架，通过场景图表示图像，用户直接操作图结构进行复杂编辑，自动生成精确提示，降低语言歧义，提升编辑质量和用户控制。
摘要：
生成式AI的最新进展使得自然语言驱动的图像编辑成为可能，但现有系统在处理包含多个交互对象的复杂场景时常常失败，因为它们严重依赖用户精心制作精确的文本提示。为了解决缺乏结构化控制的问题，我们提出了SceneCraft，一种新颖的交互式框架，通过将图像表示为可编辑的场景图来桥接用户意图和模型执行。用户无需通过试错来猜测文本提示，而是直接与可视化图交互以执行复杂的空间和关系操作。这些图修改会自动转换为精确的、上下文感知的编辑提示，有效消除语言歧义。为了确保鲁棒和多样化的结果，结构化提示被分派到多个最先进的生成模型。跨多种编辑场景的评估表明，SceneCraft提供了更直观的控制机制，显著减少了手动提示工程的认知负担，同时生成的输出在质量和保真度上获得用户一致更高的评价。
英文摘要：
Recent advances in generative AI have enabled natural language-driven image editing, yet existing systems often fail in complex scenes with multiple interacting objects because they rely heavily on users crafting precise text prompts. To address the absence of structured control, we propose SceneCraft, a novel interactive framework that bridges user intent and model execution by representing images as editable scene graphs. Instead of guessing text prompts through trial and error, users interact directly with a visual graph to perform complex spatial and relational operations. These graph modifications are automatically translated into precise, context-aware editing prompts, effectively eliminating linguistic ambiguity. To ensure robust and diverse results, structured prompts are dispatched to multiple state-of-the-art generative models. Evaluations across diverse editing scenarios show that SceneCraft provides a more intuitive control mechanism, significantly reducing the cognitive burden of manual prompt engineering while generating outputs that users consistently rate as higher in quality and fidelity.
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。
