Dimensions overall score 7.0

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• VFIG achieves state-of-the-art performance among open-source models and performs on par with GPT-5.2 on SVG conversion tasks.result 90%
• VFIG-DATA is a large-scale dataset of 66K high-quality figure-SVG pairs curated from real-world paper figures and procedurally generated diagrams.technical 95%
• VFIG uses a coarse-to-fine training curriculum combining supervised fine-tuning and reinforcement learning to optimize SVG generation.method 90%
• The model may struggle with very dense or textured images that are unsuitable for easy vectorization.limitation 85%
• VFIG-BENCH is a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures.technical 90%
• VFIG could replace manual vectorization services and disrupt current graphic design workflows by automating a time-consuming process.market 75%
• Existing datasets for figure-to-SVG conversion are typically small-scale and lack the complexity of professional diagrams.limitation 80%
• The technology could be packaged as a plugin or extension for popular design software such as Adobe Illustrator.market 70%

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