StarVector: Generating Scalable Vector Graphics Code from Images
(2312.11556)Abstract
Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development and graphic design. Existing approaches for SVG modeling using deep learning often struggle with generating complex SVGs and are restricted to simpler ones that require extensive processing and simplification. This paper introduces StarVector, a multimodal SVG generation model that effectively integrates Code Generation LLMs (CodeLLMs) and vision models. Our approach utilizes a CLIP image encoder to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens. This enables StarVector to generate unrestricted SVGs that accurately represent pixel images. To evaluate StarVector's performance, we present SVG-Bench, a comprehensive benchmark for evaluating SVG methods across multiple datasets and relevant metrics. Within this benchmark, we introduce novel datasets including SVG-Stack, a large-scale dataset of real-world SVG examples, and use it to pre-train StarVector as a large foundation model for SVGs. Our results demonstrate significant enhancements in visual quality and complexity handling over current methods, marking a notable advancement in SVG generation technology. Code and models: https://github.com/joanrod/star-vector
Overview
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StarVector introduces a new method for generating unrestricted SVG code from images using a CLIP image encoder combined with a code generation large language model (CodeLLM) dubbed StarCoder.
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The model notably improves SVG generation by uniting vision and language processing, translating visual information into SVG code effectively.
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SVG-Bench, an evaluation framework with novel datasets SVG-Emoji and SVG-Stack, is established to measure the effectiveness of SVG synthesis methods.
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Tests demonstrate that StarVector outperforms existing approaches, especially in dealing with visual quality and complexity of SVGs.
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The paper opens avenues for future research in more complex image-to-SVG conversions, text-to-SVG generation, and enhanced editing tools, potentially revolutionizing SVG creation.
Understanding StarVector: A New Approach to Scalable Vector Graphics Generation
The Challenge of SVG Generation
Scalable Vector Graphics (SVG) have become ubiquitous in digital applications, cherished for their ability to scale without loss of resolution, their editability, and compact file sizes. SVGs have particularly thrived in web development, enabling efficient rendering and file compression, and in graphic design, where they support the creation of intricate designs that retain fidelity at any size. However, generating complex SVGs has been a longstanding challenge in the field of AI—traditional methods have stumbled with complexity and have often been restricted to working with oversimplified SVGs that necessitate significant post-processing to achieve desired results.
Introducing StarVector
The innovation brought by StarVector addresses the generation of unrestricted SVG code directly from pixel-based images. It employs the CLIP image encoder to capture visual representations, transforming them into visual tokens using an adapter. The visual tokens, combined with SVG token embeddings, are processed by a code generation large language model (CodeLLM) known as StarCoder, which predicts the subsequent token in the SVG sequence, effectively aligning visual detail with SVG code elements.
By including a CLIP image encoder in its architecture, StarVector unites the realms of vision and language, translating visual elements into SVG code with proficiency. The model has been assessed using SVG-Bench, a comprehensive evaluation framework encompassing multiple datasets and metrics to measure SVG synthesis methods' effectiveness.
Contributions and Results
The contributions of the paper are:
- The development of StarVector, a powerful model for SVG generation that integrates vision and language models.
- The establishment of SVG-Bench, a unified evaluation suite that also includes two novel datasets: SVG-Emoji and SVG-Stack.
- Comprehensive testing across SVG-Bench shows StarVector's remarkable ability to generalize to complex SVGs, highlighting the benefits of pre-training on SVG-Stack for improved model performance.
The experiments demonstrate StarVector's ability to outperform current approaches and its significant improvements in handling visual quality and complexity.
A New Era for SVG Modeling
StarVector represents a pivotal step in SVG generation technology. By successfully bypassing previous limitations, future research directions are sparked, such as extending to natural image-to-SVG conversion, text-to-SVG generation, and augmented editing capabilities. The work establishes a new benchmark for SVG technology, potentially revolutionizing this domain by supporting the creation of more complex and high-quality vector images.
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GitHub - joanrod/star-vector (105 stars)