The design process of user interfaces (UIs) often begins with articulating high-level design goals. Translating these high-level design goals into concrete design mock-ups, however, requires extensive effort and UI design expertise. To facilitate this process for app designers and developers, we introduce three deep-learning techniques to create low-fidelity UI mock-ups from a natural language phrase that describes the high-level design goal (e.g. "pop up displaying an image and other options"). In particular, we contribute two retrieval-based methods and one generative method, as well as pre-processing and post-processing techniques to ensure the quality of the created UI mock-ups. We quantitatively and qualitatively compare and contrast each method's ability in suggesting coherent, diverse and relevant UI design mock-ups. We further evaluate these methods with 15 professional UI designers and practitioners to understand each method's advantages and disadvantages. The designers responded positively to the potential of these methods for assisting the design process.
翻译:用户界面(UI)的设计过程往往从阐述高层次设计目标开始。将这些高层次设计目标转换成具体的设计模型需要广泛的努力和UI设计专门知识。为了便利应用程序设计者和开发者的过程,我们引入了三种深层次的学习技术,从描述高层次设计目标的自然语言短语(例如“投放图像和其他选项”)中创建低不忠的UI模型。特别是,我们贡献了两种基于检索的方法和一种基因化方法,以及预处理和后处理技术,以确保创建的UI模拟的质量。我们从数量和质量上比较并比较了每一种方法在建议一致、多样和相关 UI设计模型方面的能力。我们进一步与15个专业的UI设计者和从业人员评估这些方法,以了解每种方法的利弊。设计者对这些方法在协助设计过程中的潜力作出了积极反应。