Adobe, MIT, Harvard University
Novel Icon Generator Tool
HCI Researcher
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Icons are prevalent from road signs to graphical menus, conveying complex concepts with simple graphical representations. However, designing such icons, particularly for a compound concept such as health insurance, requires sophisticated and creative design skills to effectively combine semantics, layouts/space and styles. In this study, we aim to automate the generation of icons given compound concepts. Informed by interviews with professional icon designers, we developed Iconate, a novel icon design tool that automatically generates icons based on textual queries and allows users to explore and customize the icons. We also introduce a computational pipeline that automatically finds typical icons for sub-concepts, and arranges them according to inferred conventions. To enable the pipeline, we also collected a new data set consisting of concepts and annotated icons. We evaluated the feasibility of the tool with designers and the validity of the pipeline through a crowd sourced experiment.

Full paper details here.


N. Zhao, Z. Bylinskii, N. W. Kim, H. Pfister, R. Lau, L. M. Herman, & J. Echevarria, “ICONATE: An Automated Approach for Compound Icon Generation and Ideation,” In ​Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, 2020.  [Under review.]

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under review
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