VisVec: A Milestone Towards Compressing Images by Converting Them to SVG using LLMs

Generating SVGs from raster images is a challenging compression task due to the semantic and structural mismatch between pixel and vector formats.We introduce VisVec, a largescale dataset of triplets: raster image, SVG vector, and textual description, designed to improve training for vision-language models (VLMs) in vector generation tasks. The initial dataset comprises 2.5k high-resolution, semantically rich examples. Our dataset aims to address key limitations in models like GPT-4V and Claude 3, which often fail to produce valid SVG outputs due to lack of structured training data. Our dataset achieves 10.52 compression ratio when using SVG over PNG. Training LLMs on it could enable high-quality image-to-SVG compression.