# A Learned Representation for Scalable Vector Graphics

## TL;DR

This paper introduces a sequential generative model for vector graphics that provides a scale-invariant, manipulable representation of fonts, aiding font design and style transfer.

## Contribution

It presents a novel sequential generative model for vector graphics that captures font styles and dependencies, enabling systematic manipulation and style propagation.

## Key findings

- Model captures statistical dependencies in font datasets
- Provides a scale-invariant, manipulable font representation
- Facilitates font style transfer and design

## Abstract

Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for graphic designers to facilitate font design.