StarVector

StarVector redefines SVG generation by integrating visual and linguistic processing through a multimodal architecture. This innovative approach treats vectorization as a code generation task, leveraging the full richness of SVG syntax. StarVector excels at converting both images and text instructions into high-quality SVG code, handling diverse visual inputs from simple icons to intricate technical diagrams.

The model employs a vision-language architecture, where an image encoder and language decoder collaboratively understand image semantics, recognizing shapes, hierarchies, and layers to produce structured SVG outputs. This advanced architecture enables StarVector to generate complex SVG elements, such as text and intricate paths, directly from images with remarkable precision.

StarVector's foundation is built upon the extensive SVG-Stack dataset, consisting of over 2 million SVG samples, ensuring robust training and consistent performance across various graphic styles. Evaluated through SVG-Bench, a comprehensive evaluation framework, StarVector significantly outperforms existing methods in both text-to-SVG and image-to-SVG generation tasks.

The model's architecture includes a Vision Transformer (ViT) for processing image patches and an LLM Adapter that projects embeddings into visual tokens for integration with the language model. This unified framework allows StarVector to leverage the strengths of both visual and textual modalities, resulting in accurate and contextually appropriate SVG generation.

StarVector's superior performance is evident in its ability to produce clean, detailed, and structurally coherent vector graphics, setting a new standard in the field. The model's capacity to understand and reproduce complex vector graphics makes it particularly valuable for applications requiring precise vectorization of icons, logos, and technical diagrams.