What is StarVector
StarVector is an open-source multimodal vision-language model jointly developed by ServiceNow Research, Mila – Quebec AI Institute, and ETS Montreal, focused on converting images and text into Scalable Vector Graphics (SVG) code. The model adopts a multimodal architecture that can simultaneously process image and text information, operating directly in the SVG code space to generate standard, editable SVG files. StarVector is trained on the SVG-Stack dataset containing over 2 million SVG samples, and is available in two scales, StarVector-1B and StarVector-8B, to meet different needs.
Main Features of StarVector
- Image-to-SVG Conversion (Image-to-SVG): Can directly convert images into SVG code, achieving image vectorization.
- Text-to-SVG Generation (Text-to-SVG): Can generate corresponding SVG graphics based on text instructions.
Technical Principles of StarVector
- Multimodal Architecture: StarVector adopts a multimodal architecture that seamlessly integrates vision and language models. Visual features of images are extracted through a vision encoder (such as Vision Transformer or CLIP image encoder), and then these features are mapped to the embedding space of the language model through an adapter, generating visual tokens. These visual tokens, together with text embeddings, are input into the language model, achieving unified processing of images and text.
- Image Encoding and Visual Token Generation: The image encoder (such as Vision Transformer) splits the input image into small patches and converts them into hidden features. These are projected into the embedding space of the language model through a non-linear adapter, forming visual tokens. It can capture key visual features of the image, such as shape, color distribution, and structural layout.
- Language Model and SVG Code Generation: StarVector uses a language model based on StarCoder. During training, the model undergoes supervised learning through the next-token prediction task on SVG code. In the inference stage, the model autoregressively predicts SVG code based on the visual tokens of the input image.
- Large-Scale Dataset Training: StarVector is trained on the SVG-Stack dataset containing over 2 million SVG samples. The dataset covers diverse SVG samples and supports diverse tasks of image-to-SVG and text-to-SVG. StarVector introduces the SVG-Bench evaluation benchmark for comprehensive evaluation of model performance.
- Performance Advantages: StarVector performs excellently in image-to-SVG and text-to-SVG tasks. The generated SVG files are more compact and semantically richer, effectively utilizing SVG primitives. In the SVG-Bench benchmark test, StarVector outperforms traditional methods and deep learning baseline models on multiple metrics.
StarVector Project Address
- Project Official Website：https://starvector.github.io/
- Github Repository：https://github.com/joanrod/star-vector
- arXiv Technical Paper：https://arxiv.org/pdf/2312.11556
Application Scenarios of StarVector
- Icon Generation: Quickly generate SVG icons based on text descriptions or image inputs, for use in webpage navigation bars, buttons, etc.
- Artistic Creation: Artists can use StarVector to transform creative sketches or text descriptions into vector art works, facilitating subsequent editing and modification.
- Animation Production: The generated SVG graphics can serve as basic elements for animation production, further developed into dynamic effects.
- Programming Education: Students can learn SVG code generation and editing through StarVector, improving their programming and graphic design skills.
- Technical Diagram Generation: Generate technical diagrams from text descriptions, such as flowcharts, structure diagrams, etc., for engineering documents and technical explanations.
- Data Visualization: Visualize data as SVG graphics for easy display on web pages or in reports, while maintaining the editability and scalability of the graphics.