coDrawAgents：一种用于组合图像生成的多智能体对话框架

中文摘要 AI 辅助

coDrawAgents通过四类智能体协作提升复杂场景中多对象生成的准确性与属性保持，实验表明其在文本-图像对齐、空间准确性和属性绑定方面表现更优。

英文摘要

Text-to-image generation has advanced rapidly, but existing models still struggle with faithfully composing multiple objects and preserving their attributes in complex scenes. We propose coDrawAgents, an interactive multi-agent dialogue framework with four specialized agents: Interpreter, Planner, Checker, and Painter that collaborate to improve compositional generation. The Interpreter adaptively decides between a direct text-to-image pathway and a layout-aware multi-agent process. In the layout-aware mode, it parses the prompt into attribute-rich object descriptors, ranks them by semantic salience, and groups objects with the same semantic priority level for joint generation. Guided by the Interpreter, the Planner adopts a divide-and-conquer strategy, incrementally proposing layouts for objects with the same semantic priority level while grounding decisions in the evolving visual context of the canvas. The Checker introduces an explicit error-correction mechanism by validating spatial consistency and attribute alignment, and refining layouts before they are rendered. Finally, the Painter synthesizes the image step by step, incorporating newly planned objects into the canvas to provide richer context for subsequent iterations. Together, these agents address three key challenges: reducing layout complexity, grounding planning in visual context, and enabling explicit error correction. Extensive experiments on benchmarks GenEval and DPG-Bench demonstrate that coDrawAgents substantially improves text-image alignment, spatial accuracy, and attribute binding compared to existing methods.

发表机构

• Lingnan University（岭南大学）
• CMU（卡内基梅隆大学）
• CUHK（香港中文大学）
• Alibaba DAMO Academy（阿里巴巴达摩院）
• SJTU（上海交通大学）
• HKU（香港大学）
• China University of Petroleum（中国石油大学）

机构由 AI 辅助整理，请以论文原文为准。

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Accepted to CVPR 2026 Findings