DrawAI: Agentic Benchmark and Workflow for Making Raster Images Editable
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 ...
| Authors | Pu Cao, Qingye Kong, Xuedan Yin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 August 2026 |
| Last updated at source | 1 August 2026 |
Models and datasets referencing this paper
- caopu/DrawAI-Bench-LiteDataset
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