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Q
Qijia He
University of Washington
X
Xunmei Liu
University of Washington
H
Hammaad Memon
University of Washington
Z
Ziang Li
University of Washington
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Founder's Pitch
"VFig automates conversion of complex raster images into editable SVGs, streamlining vector graphics editing and scaling."
Commercial Viability Breakdown
0-10 scaleHigh Potential
4/4 signals
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4/4 signals
Series A Potential
4/4 signals
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Why It Matters
This research automates the conversion of rasterized technical figures back into editable vector formats, cutting down on manual design time and expertise required, thus promoting scalability and adaptability in digital designs.
Product Angle
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.
Disruption
This tool could replace manual vectorization services and disrupt current graphic design workflows by automating a time-consuming process.
Product Opportunity
The market for graphic design tools is vast, encompassing professionals in tech, media, and academia who need scalable, editable graphics. Licensing to design software companies or selling a standalone product could capitalize on the widespread need for efficient design workflows.
Use Case Idea
A design tool for graphic designers in engineering and education to quickly convert lost vector source files back into SVGs, allowing for easy updates and modifications.
Science
The paper presents VFig, a system that uses Vision-Language Models to convert complex raster images into structured SVGs, leveraging a dataset of 66,000 figures and employing a learning strategy that combines supervised fine-tuning and reinforcement learning to optimize fidelity.
Method & Eval
VFig was tested using a newly proposed evaluation suite tailored for SVG conversion quality, benchmarking against other models and achieving a high VLM-Judge score of 0.829.
Caveats
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.
Author Intelligence
Qijia He
LEAD University of Washington
Xunmei Liu
LEAD University of Washington
Hammaad Memon
LEAD University of Washington
Ziang Li
LEAD University of Washington
Zixian Ma
Allen Institute for Artificial Intelligence
Jaemin Cho
Allen Institute for Artificial Intelligence
Jason Ren
University of Washington
Daniel S Weld
Allen Institute for Artificial Intelligence
Ranjay Krishna
University of Washington