Is SVG the Final Frontier? (svg.new)

Vectorizing images into scalable shape-based representations, particularly through SVGs, is a challenging but significant frontier in AI/ML. While large language models (LLMs) excel at generating text and manipulating code, their attempts at creating vector graphics have not yet yielded impressive results. Despite jokes about SVG generation being the "final frontier" for AI, practical applications remain limited, as exemplified by Simon Willison's humorous observation on the lack of improvement in AI-generated images of pelicans. In contrast, significant advancements have been made in raster image generation through diffusion models, cementing their supremacy in AI-generated imagery. Various research efforts like LLM4SVG, StarVector, and OmniSVG are exploring ways to harness LLMs and vision-language models for effective SVG generation, but these projects remain in the experimental stage, often hindered by slow processing speeds and lower-quality outputs. Consequently, for those looking to incorporate AI into their vector graphic workflows, the most effective current approach involves using advanced image models in tandem with robust image vectorizers in a two-step process. As AI continues to evolve, there's potential for future integrations of vectorization techniques into foundational models, potentially enhancing the capability of AI in handling vector graphics.