Learning to draw vector graphics : applying generative modeling to font glyphs

Today, designers work in tandem with computerized tools to create stylized graphic designs, diagrams, and icons. In this work, we explore the applications of generative modeling to the design of vectorized drawings, with a focus on font glyphs. We establish a data-driven approach for creating preliminary graphics upon which designers can iterate. To accomplish this, we present an end-to-end pipeline for a supervised training system on Scalable Vector Graphics (SVGs) that learns to reconstruct training data and produce similar but novel examples. We demonstrate its results on selected characters using a Google Fonts dataset of 2552 font faces. Our approach uses a variational autoencoder to learn sequences of SVG drawing commands and is capable of both recreating ground truth inputs and generating unseen, editable SVG outputs. To investigate improvements to model performance, we perform two experiments: one on the effects of various SVG feature encodings on generated outputs, and one on a modified architecture that explicitly encodes style and class separately for multi-class generation.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2018

Author dc:creator

• Zhong, Kimberli

Advisor dc:contributor.advisor

• Frédo Durand.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119692

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119692