I would like to request a native ChatGPT mode for high-fidelity image-to-SVG reconstruction and editing.
The goal is not simple raster-to-vector tracing, but recovering clean, editable visual structure from complex images: layers, groups, text, arrows, connectors, primitives, paths, and semantic hierarchy.
This would let users upload a diagram, logo, UI screenshot, scientific figure, architecture chart, or infographic and ask ChatGPT to convert it into a clean SVG, then edit it through natural language.
A strong implementation could combine supervised SVG training, synthetic image/SVG pairs, a canonical intermediate representation, rendering-aware reinforcement learning, differentiable rendering, hybrid rewards for fidelity/simplicity/editability, and an iterative preview/edit loop.
Recent research such as DeepSVG, diffvg, StarVector, VGBench, SVGenius, Rendering-Aware RL for Vector Graphics Generation, Reason-SVG, and VFig suggests that SVG generation should be treated as structured visual-program reconstruction, not just text generation or ordinary image generation.
This feature would be useful for designers, developers, engineers, researchers, educators, documentation teams, accessibility workflows, and enterprise users. It would turn ChatGPT into a bridge from pixels to editable visual systems.