SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers

2024-12-13

arXiv.org

AI Summary

Existing SVG generation methods suffer from high computational overhead and struggle to simultaneously achieve high fidelity and fine-grained controllability, particularly lacking efficient approaches for color-rich SVG synthesis. To address this, we propose the first compositional SVG generation paradigm, modeling SVGs as geometrically and semantically aware discrete component sequences, integrated with a text-guided autoregressive Transformer and XML-structured sequence modeling. We introduce and publicly release ColorSVG-100K—the first large-scale, color-annotated SVG dataset. Our method accelerates generation by 604× over conventional optimization-based approaches, while outperforming state-of-the-art methods on FID, CLIP-Score, and human evaluation. It enables high-fidelity, editable, and interactive color SVG output, significantly improving both generation efficiency and design controllability.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Scalable Vector Graphics (SVG) are essential XML-based formats for versatile graphics, offering resolution independence and scalability. Unlike raster images, SVGs use geometric shapes and support interactivity, animation, and manipulation via CSS and JavaScript. Current SVG generation methods face challenges related to high computational costs and complexity. In contrast, human designers use component-based tools for efficient SVG creation. Inspired by this, SVGBuilder introduces a component-based, autoregressive model for generating high-quality colored SVGs from textual input. It significantly reduces computational overhead and improves efficiency compared to traditional methods. Our model generates SVGs up to 604 times faster than optimization-based approaches. To address the limitations of existing SVG datasets and support our research, we introduce ColorSVG-100K, the first large-scale dataset of colored SVGs, comprising 100,000 graphics. This dataset fills the gap in color information for SVG generation models and enhances diversity in model training. Evaluation against state-of-the-art models demonstrates SVGBuilder's superior performance in practical applications, highlighting its efficiency and quality in generating complex SVG graphics.

Problem

• High computational cost in SVG generation
• Lack of large-scale colored SVG datasets
• Inefficiency in traditional SVG creation methods

Innovation

• Component-based autoregressive model for SVG generation
• Text-guided generation from textual input
• ColorSVG-100K dataset enhances training diversity