TY - JOUR
T1 - PlotThread
T2 - Creating expressive storyline visualizations using reinforcement learning
AU - Tang, Tan
AU - Li, Renzhong
AU - Wu, Xinke
AU - Liu, Shuhan
AU - Knittel, Johannes
AU - Koch, Steffen
AU - Yu, Lingyun
AU - Ren, Peiran
AU - Ertl, Thomas
AU - Wu, Yingcai
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2021/2
Y1 - 2021/2
N2 - Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.
AB - Storyline visualizations are an effective means to present the evolution of plots and reveal the scenic interactions among characters. However, the design of storyline visualizations is a difficult task as users need to balance between aesthetic goals and narrative constraints. Despite that the optimization-based methods have been improved significantly in terms of producing aesthetic and legible layouts, the existing (semi-) automatic methods are still limited regarding 1) efficient exploration of the storyline design space and 2) flexible customization of storyline layouts. In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines. Based on the framework, we introduce PlotThread, an authoring tool that integrates a set of flexible interactions to support easy customization of storyline visualizations. To seamlessly integrate the AI agent into the authoring process, we employ a mixed-initiative approach where both the agent and designers work on the same canvas to boost the collaborative design of storylines. We evaluate the reinforcement learning model through qualitative and quantitative experiments and demonstrate the usage of PlotThread using a collection of use cases.
KW - Storyline visualization
KW - mixed-initiative design
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85100372708&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2020.3030467
DO - 10.1109/TVCG.2020.3030467
M3 - Article
C2 - 33048748
AN - SCOPUS:85100372708
SN - 1077-2626
VL - 27
SP - 294
EP - 303
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 2
M1 - 9222335
ER -