Many digital design tasks require a user to set a large number of parameters. Gallery-based interfaces provide a way to quickly evaluate examples and explore the space of potential designs, but require systems to predict which designs from a high-dimensional space are the right ones to present to the user. In this paper we present the design adjectives framework for building parameterized design tools in high dimensional design spaces. The framework allows users to create and edit design adjectives, machine-learned models of user intent, to guide exploration through high-dimensional design spaces. We provide a domain-agnostic implementation of the design adjectives framework based on Gaussian process regression, which is able to rapidly learn user intent from only a few examples. Learning and sampling of the design adjective occurs at interactive rates, making the system suitable for iterative design workflows. We demonstrate use of the design adjectives framework to create design tools for three domains: materials, fonts, and particle systems. We evaluate these tools in a user study showing that participants were able to easily explore the design space and find designs that they liked, and in professional case studies that demonstrate the framework’s ability to support professional design concepting workflows.


To Appear at UIST 2020