CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration
This paper introduces CanvasAgent, a tool-augmented multimodal agent that learns to orchestrate heterogeneous visual tools through multi-turn interaction for complex image creation and editing. The authors also present CanvasCraft, a large-scale dataset with 140K executable trajectories and 10K RL task specifications. The agent is trained with supervised fine-tuning and then optimized with GRPO using a hybrid reward combining outcome- and process-level signals. Experiments demonstrate effectiveness in both final image quality and trajectory behavior.
-->
[Submitted on 6 Jul 2026]
Title:CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration
View a PDF of the paper titled CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration, by Hairui Zhu and Yiying Yang and Tengjin Weng and Ziyu Lu and Xiao Yao and Xiaoyang Ye and Lin Ma and Wenhao Jiang
View PDF HTML (experimental)
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.
Comments: 18pages, 5 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05465 [cs.CV]
(or arXiv:2607.05465v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.05465
arXiv-issued DOI via DataCite
Submission history
From: HaiRui Zhu [view email] [v1] Mon, 6 Jul 2026 04:57:18 UTC (2,477 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration, by Hairui Zhu and Yiying Yang and Tengjin Weng and Ziyu Lu and Xiao Yao and Xiaoyang Ye and Lin Ma and Wenhao Jiang
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-07
Change to browse by:
cs cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)