coDrawAgents: A Multi-Agent Dialogue Framework for Compositional Image Generation

2026-03-13

AI Summary

Existing text-to-image generation models struggle to accurately compose multiple objects in complex scenes while preserving their spatial relationships and attribute consistency. To address this challenge, this work proposes a multi-agent collaborative framework comprising an Interpreter, Planner, Checker, and Painter. The framework enables hierarchical and iterative image synthesis through semantic parsing, semantic-saliency-based object grouping, incremental layout planning grounded in visual context, and explicit verification of spatial and attribute consistency. Evaluated on the GenEval and DPG-Bench benchmarks, the proposed method achieves significant improvements in text-image alignment, spatial accuracy, and attribute binding fidelity.

Technology Category

• Multiagent Systems: Multiagent Planning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• compositional image generation
• text-to-image alignment
• spatial accuracy
• attribute binding
• multi-object composition

Innovation

• multi-agent dialogue
• compositional image generation
• layout-aware generation
• explicit error correction
• semantic salience