#56 · Primary category: Image Generation

star-vector

llm multimodal-large-language-models svg vlm

StarVector is a foundation model for SVG generation that transforms vectorization into a code generation task. Using a vision-language modeling architecture, StarVector processes both visual and textual inputs to produce high-quality SVG code with remarkable precision.

Project last updated:11/07/25

GitHub Stars

4.6K

Forks

258

Contributors

8

License

Apache-2.0

Why we included this project

Converting a logo or icon into clean, scalable SVG by hand is slow, and most tracing tools hand back bloated or messy paths. StarVector treats that as a code generation problem: a vision-language model reads the image and writes out SVG primitives directly, so the output stays compact rather than being a rough curve approximation. It can also generate fresh SVG from a text prompt alone, which helps when there is no source artwork to start from. The results are most dependable for iconography, logotypes, technical diagrams, and charts, where the code stays semantically meaningful instead of turning into a sprawling path dump. Teams that regularly produce or re-skin vector assets for UIs, dashboards, or documentation get a way to automate that step and keep everything resolution-independent.

Articles for this project

No articles for this project yet.

To suggest a topic or contribute an article, contact us.

Related projects in this category