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 designers to facilitate font design. moveTo (15, 25) lineTo (-2, 0. 3) cubicBezier (-7.4, 0. 2) (-14.5, 11.7), (-12.1, 23.4) ... (Ferraiolo, 2001 ) that enables such manipulations. All images are samples from this generative model.The last few years have witnessed dramatic advances in generative models of images that produce near photographic quality imagery of human faces, animals, and natural objects (Radford et al., 2015; Goodfellow et al., 2014; Brock et al., 2018; Karras et al., 2018; Kingma & Dhariwal, 2018) . These models provide an exhaustive characterization of natural image statistics (Simoncelli & Olshausen, 2001 ) and represent a significant advance in this domain. However, these advances in image synthesis ignore an important facet of how humans interpret raw visual information (Reisberg & Snavely, 2010) , namely that humans seem to exploit structured representations of visual concepts (Lake et al., 2017; Hofstadter, 1995) . Structured representations may be readily employed to aid generalization and efficient learning by identifying higher level primitives for conveying visual information (Lake et al., 2015) or provide building blocks for creative exploration (Hofstadter, 1995; Hofstadter & McGraw, 1993) . This may be best seen in human drawing, where techniques such as gesture drawing emphasize parsimony for capturing higher level semantics and actions with minimal graphical content (Stanchfield, 2007) .In this work, we focus on an subset of this domain where we think we can make progress and improve the generality of the approach. Font generation represents a 30 year old problem posited as a constrained but diverse domain for understanding high level perception and creativity (Hofstadter, 1995) . Early research attempted to heuristically systematize the creation of fonts for expressing the identity of characters (e.g. a, 2) as well as stylistic elements constituting the "spirit" of a font (Hofstadter & McGraw, 1993) . Despite providing great inspiration, the results were limited by a reliance on heuristics and a lack of a learned, structured representation (Rehling, 2001) . Subsequent work for learning font representations focused on models with simple parameterizations (Lau, 2009), template matching (Suveeranont & Igarashi, 2010) , example-based hints (Zongker et al., 2000) , or more recently, learning manifolds for geometric annotations (Campbell & Kautz, 2014) .We instead frame the problem of generating fonts by specifying it with Scalable Vector Graphics (SVG) -a common file format for fonts, human drawings, designs and illustrations (Ferraiolo, 2001) . SVGs are a compact, scale-invariant representation that may be rendered on most web browsers. SVGs specify an illustration as a sequence of a higher-level commands paired with numerical arguments FIG0 . We take inspiration from the literature on generative models of images in rasterized pixel space (Graves, 2013; Van den Oord et al., 2016) . Such models provide powerful auto-regressive formulations for discrete, sequential data (Hochreiter & Schmidhuber, 1997; Graves, 2013; Van den Oord et al., 2016) and may be applied to rasterized renderings of drawings (Ha & Eck, 2017) . We extend these approaches to the generation of sequences of SVG commands for the inference of individual font characters. The goal of this work is to build a tool to learn a representation for font characters and style that may be extended to other artistic domains (Clouâtre & Demers, 2019; Sangkloy et al., 2016; Ha & Eck, 2017) , or exploited as an intelligent assistant for font creation (Carter & Nielsen, 2017) .Our main contributions are: 1) Build a generative model for scalable vector graphics (SVG) images and apply this to a large-scale dataset of 14 M font characters. 2) Demonstrate that the generative model provides a latent representation of font styles that captures a large amount of diversity and is consistent across individual characters. 3) Exploit the latent representation from the model to infer complete SVG fontsets from a single character. 4) Identify semantically meaningful directions in the latent representation to globally manipulate font style. In the work we presented a generative model for 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 the limitations of a sequential, stochastic model for capturing the statistical dependencies and richness of this dataset. Even in its present form, the current model may be employed as an assistive agent for helping humans design fonts in a more time-efficient manner (Carter & Nielsen, 2017; Rehling, 2001 ).
We attempt to model the drawing process of fonts by building sequential generative models of vector graphics (SVGs), a highly structured representation of font characters.
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