FaceOff: Assisting the Manifestation Design of Web Graphical User Interface
Designing desirable and aesthetical manifestation of web graphic user interfaces (GUI) is a challenging task for web developers. We propose FaceOff, a data-driven automation system that assists the manifestation design of web GUI. FaceOff constructs a repository of web GUI templates based on popular websites and professional design examples. Given a web page for designing manifestation, FaceOff first segments it into multiple blocks, and retrieves GUI templates in the repository for each block. Subsequently, FaceOff recommends multiple combinations of templates according to a Convolutional Neural Network (CNN) based style-embedding model, which makes the recommended style combinations diverse and accordant. We demonstrate that FaceOff can retrieve suitable GUI templates with well-designed and harmonious style, and thus alleviate the developer efforts.
The source code and dataset will be update soon.