FlatMagic: Improving Flat Colorization through AI-driven Design for Digital Comic Professionals

要旨

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as 'flatting,' or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations---technical and UX---to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals' real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.

著者
Chuan Yan
George Mason University, Fairfax, Virginia, United States
John Joon Young. Chung
University of Michigan, Ann Arbor, Michigan, United States
Yoon Kiheon
Pusan National University, Pusan, Korea, Republic of
Yotam Gingold
George Mason University, Fairfax, Virginia, United States
Eytan Adar
University of Michigan, Ann Arbor, Michigan, United States
Sungsoo Ray Hong
George Mason University, Fairfax, Virginia, United States
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3502075

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Creativity Support

393
5 件の発表
2022-05-02 23:15:00
2022-05-03 00:30:00