VFIG: Vectorizing Complex Figures in SVG with Vision-Language Models

Dimensions overall score 7.0

• 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%
  Evidencepartial

  VFIG achieves state-of-the-art performance among open-source models and performs on par with GPT-5.2, achieving a VLM-Judge score of 0.829 on VFIG-BENCH.

  Implicationpartial

  Explicitly stated in the abstract with specific metric reference

  Verificationpartial

  partial

• 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%
  Evidencepartial

  We address this by introducing VFIG-DATA, a large-scale dataset of 66K high-quality figure-SVG pairs, curated from a diverse mix of real-world paper figures and procedurally generated diagrams.

  Implicationpartial

  Directly stated in the abstract with specific numeric data

  Verificationpartial

  partial

• VFIG uses a coarse-to-fine training curriculum combining supervised fine-tuning and reinforcement learning to optimize SVG generation.method 90%
  Evidencepartial

  We introduce a coarse-to-fine training curriculum that begins with supervised fine-tuning (SFT) to learn atomic primitives and transitions to reinforcement learning (RL) refinement to optimize global diagram fidelity, layout consistency, and topological edge cases.

  Implicationpartial

  Explicitly described in the abstract with specific methodology details

  Verificationpartial

  partial

• The model may struggle with very dense or textured images that are unsuitable for easy vectorization.limitation 85%
  Evidencepartial

  The model may struggle with very dense or textured images that are unsuitable for easy vectorization and the conversion accuracy for extremely complex structures may degrade.

  Implicationpartial

  Directly stated in the analysis excerpt as a caveat

  Verificationpartial

  partial

• VFIG-BENCH is a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures.technical 90%
  Evidencepartial

  Finally, we introduce VFIG-BENCH, a comprehensive evaluation suite with novel metrics designed to measure the structural integrity of complex figures.

  Implicationpartial

  Explicitly stated in the abstract with clear purpose description

  Verificationpartial

  partial

• VFIG could replace manual vectorization services and disrupt current graphic design workflows by automating a time-consuming process.market 75%
  Evidencepartial

  This tool could replace manual vectorization services and disrupt current graphic design workflows by automating a time-consuming process.

  Implicationpartial

  Stated in the analysis excerpt as potential disruption, but requires market validation

  Verificationpartial

  partial

• Existing datasets for figure-to-SVG conversion are typically small-scale and lack the complexity of professional diagrams.limitation 80%
  Evidencepartial

  While this task is inherently data-driven, existing datasets are typically small-scale and lack the complexity of professional diagrams.

  Implicationpartial

  Directly stated in the abstract as motivation for creating VFIG-DATA

  Verificationpartial

  partial

• The technology could be packaged as a plugin or extension for popular design software such as Adobe Illustrator.market 70%
  Evidencepartial

  Package the technology as a plugin or extension for popular design software such as Adobe Illustrator, enabling users to convert raster images to SVGs seamlessly.

  Implicationpartial

  Suggested in the analysis excerpt as a product angle, but speculative regarding implementation

  Verificationpartial

  partial

Competitive landscape

VFIG converts rasterized images of complex figures into editable SVGs using a novel vision-language model and a large-scale dataset, bridging the gap for designers and researchers.