Magic Layouts: Structural Prior for Component Detection in User Interface Designs
This addresses the problem of robust UI component detection for designers and developers, enabling rapid digital prototyping of user experience designs, though it appears incremental as it builds on existing detectors.
The paper tackles the problem of parsing screenshots or hand-drawn sketches of user interface layouts by extending existing detectors with a learned structural prior that encodes spatial co-occurrence relationships between UI components, resulting in performance gains for UI layout parsing.
We present Magic Layouts; a method for parsing screenshots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn a prior over mobile UI layouts, encoding common spatial co-occurrence relationships between different UI components. Conditioning region proposals using this prior leads to performance gains on UI layout parsing for both hand-drawn UIs and app screenshots, which we demonstrate within the context an interactive application for rapidly acquiring digital prototypes of user experience (UX) designs.