StarVector is a multimodal foundation model designed for generating Scalable Vector Graphics (SVG) from images or textual descriptions. The system treats vector graphics creation as a code generation problem, producing SVG code that can render detailed vector images. Its architecture combines computer vision techniques with language modeling capabilities so it can understand visual inputs and textual prompts simultaneously. The model converts raster images or text instructions into structured vector representations, enabling high-quality vectorization and design generation. This approach allows StarVector to create scalable graphics that maintain visual quality regardless of resolution, which is especially useful for design tools and illustration workflows. Because the model produces SVG code rather than pixel images, the output can be edited programmatically or integrated directly into web and design environments.
Features
- Multimodal model capable of generating SVG graphics from images or text
- Vision-language architecture combining visual perception and code generation
- Conversion of raster images into editable vector graphics
- Text-guided generation of scalable vector illustrations
- Output in SVG code format suitable for web and design applications
- Training and inference pipelines for vector graphics generation models