  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.
