CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

2026-07-06

AI Summary

Existing agents struggle to coordinate diverse visual tools for complex image creation and editing due to the absence of large-scale, executable trajectory supervision. To address this, this work introduces CanvasAgent, an intelligent agent, along with CanvasCraft—the first large-scale dataset of executable trajectories tailored for complex image generation. The approach integrates supervised fine-tuning (SFT) and generalized reinforcement policy optimization (GRPO), incorporating multimodal understanding, intermediate result validation, visual asset tracking, and heterogeneous tool scheduling. A hybrid reinforcement learning strategy is further designed, combining outcome-based and process-based rewards. Experimental results demonstrate that the proposed method significantly outperforms baseline approaches in both final image quality and trajectory plausibility, validating its effectiveness in multi-tool collaborative visual creation.

Abstract

Complex image creation and editing often require more than a single generation or editing model. A user request may involve synthesizing images, localizing objects, segmenting regions, editing selected content, compositing intermediate assets, reading text, and enhancing the final result. Such tasks shift multimodal agents from perception-augmented reasoning to manipulation-centered visual creation, where tools must actively transform visual states rather than merely inspect them. However, existing multimodal tool-use agents are mostly optimized for perception, search, or domain-specific editing, and lack large-scale supervision for executable image-creation trajectories. In this paper, we introduce CanvasCraft, a large-scale multimodal tool-use dataset for complex image creation and editing, and \textbf{CanvasAgent}, a tool-augmented multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interaction. CanvasCraft contains 140K fully annotated executable trajectories and 10K RL task specifications. CanvasAgent is first trained with SFT to learn executable reasoning-action trajectories, and is then optimized with GRPO using a hybrid reward that combines outcome- and process-level signals. During rollout, CanvasAgent inspects intermediate results, tracks visual assets, and adapts tool decisions to the evolving visual state. Experiments evaluate both final image quality and trajectory behavior, demonstrating the effectiveness of CanvasAgent and the proposed dataset for complex multi-tool image creation workflows.

Problem

• multimodal agents
• image creation
• visual tool orchestration
• executable trajectories
• complex editing

Innovation

• visual tool orchestration
• multimodal agent
• executable trajectories
• image creation and editing
• GRPO optimization