Problem
Research questions and friction points this paper is trying to address.
image-to-editable reconstruction
raster image editing
structured graphics recovery
visual content manipulation
editable artifact generation
DrawAI: Agentic Benchmark and Workflow for Making Raster Images Editable
🤖 AI Summary
Current image generation models produce raster images that are difficult to edit, as their content is flattened into pixels, precluding inspection, modification, or reuse of individual components. This work introduces the novel task of “image-to-editable reconstruction” and proposes DrawAI-Flow, a two-stage agent-based workflow that recovers structured, directly manipulable graphic representations from raster images while preserving visual fidelity. We establish DrawAI-Bench, the first benchmark for this task, and design an iterative pipeline integrating parsing and reconstruction agents. Our approach employs a multimodal agent architecture coupled with a code rendering–verification–correction loop to generate executable graphic code. Evaluation across 13 models and five agent frameworks on DrawAI-Bench demonstrates that DrawAI-Flow significantly improves reconstruction quality, highlighting the critical roles of model capabilities, framework selection, and workflow design.
📝 Abstract
Recent image-generation models and multimodal agents can produce high-quality visuals for increasingly complex visual communication tasks. Yet their raster outputs remain difficult to use directly because meaningful content and relationships are flattened into pixels, preventing users from inspecting, modifying, rearranging, or reusing individual components. We formulate image-to-editable reconstruction, which recovers a structured, directly manipulable artifact from a raster image while preserving its visual and semantic content. The central challenge is to jointly satisfy Fidelity and Editability, which often trade off in practice. To study this task, we introduce DrawAI, comprising an agentic benchmark, DrawAI-Bench, and a reconstruction workflow, DrawAI-Flow. DrawAI-Bench spans scientific figures, presentation slides, posters, and diagrams, combining real and AI-generated images to reflect practical visual-creation scenarios. It evaluates Fidelity and Editability through a hybrid protocol of 39 criteria: deterministic rule-based metrics measure properties with direct correspondences, while asset-specific vision-language rubrics capture semantic and perceptual qualities for which exact matching is misleading. Besides, we propose DrawAI-Flow, a two-stage agentic workflow in which a Parser Agent turns extracted elements evidence into an explicit reconstruction plan, and a Reconstruction Agent realizes the plan as executable graphics code through an iterative code-render-validate-revise loop. On DrawAI-Bench, we systematically evaluate thirteen models across five agent harnesses to study the effects of model capability, harness choice, and workflow design. The results show that reconstruction quality and costs vary substantially across model-harness configurations, while DrawAI-Flow consistently improves editable structure.
Innovation
Methods, ideas, or system contributions that make the work stand out.
image-to-editable reconstruction
agentic benchmark
structured graphics recovery
multimodal agent workflow
fidelity-editability trade-off
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