Sketch-based Creativity Support Tools using Deep Learning
This work addresses the problem of enhancing creative processes for designers and artists through sketch-based tools, but it appears incremental as it adapts existing techniques and focuses on specific applications.
The paper tackles the development of deep-learning-driven creativity support tools for sketching by creating a new paired dataset of sketches and mobile user interfaces, building a sketch-based retrieval system, and introducing a conversational sketching system with natural-language interaction. It presents qualitative and quantitative results, though specific numbers are not provided in the abstract.
Sketching is a natural and effective visual communication medium commonly used in creative processes. Recent developments in deep-learning models drastically improved machines' ability in understanding and generating visual content. An exciting area of development explores deep-learning approaches used to model human sketches, opening opportunities for creative applications. This chapter describes three fundamental steps in developing deep-learning-driven creativity support tools that consumes and generates sketches: 1) a data collection effort that generated a new paired dataset between sketches and mobile user interfaces; 2) a sketch-based user interface retrieval system adapted from state-of-the-art computer vision techniques; and, 3) a conversational sketching system that supports the novel interaction of a natural-language-based sketch/critique authoring process. In this chapter, we survey relevant prior work in both the deep-learning and human-computer-interaction communities, document the data collection process and the systems' architectures in detail, present qualitative and quantitative results, and paint the landscape of several future research directions in this exciting area.