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The design process of user interfaces (UIs) often begins with articulatinghigh-level design goals. Translating these high-level design goals intoconcrete design mock-ups, however, requires extensive effort and UI designexpertise. To facilitate this process for app designers and developers, weintroduce three deep-learning techniques to create low-fidelity UI mock-upsfrom a natural language phrase that describes the high-level design goal (e.g."pop up displaying an image and other options"). In particular, we contributetwo retrieval-based methods and one generative method, as well aspre-processing and post-processing techniques to ensure the quality of thecreated UI mock-ups. We quantitatively and qualitatively compare and contrasteach method's ability in suggesting coherent, diverse and relevant UI designmock-ups. We further evaluate these methods with 15 professional UI designersand practitioners to understand each method's advantages and disadvantages. Thedesigners responded positively to the potential of these methods for assistingthe design process.

Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models