A Scalable Vector Graphics Latent Space

Scalable Vector Graphics are a fundamental medium for reso-lution-independent visual content, yet the deep learning community lacksa continuous, dense, and invertible latent space for vector representa-tions, the kind of foundational building block that Variational Autoen-coders and their descendants have long provided for raster images. Weintroduce SLS (SVG Latent Space), a Transformer-based autoencoderthat learns compact dense representations of individual SVG paths, theatomic visual elements from which any SVG image can be composed. Bymodeling SVG commands, coordinate data, and visual properties withina unified BPE-based token vocabulary, SLS learns fixed-size latent rep-resentations that jointly capture structure and appearance, and can bedecoded back into valid, style-consistent SVG paths with high fidelity.The resulting embedding space is robust, invertible, and structured: em-beddings lie on a unit hypersphere, enabling efficient similarity search,composition, and downstream conditioning through simple vector-spaceoperations. Finally, we demonstrate that SLS generalizes across diversetasks reducing their FLOPs by over 150× compared to token-based ap-proaches, and establishing a general-purpose latent foundation for vectorgraphics research.