Title
PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning
Abstract
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.
Year
DOI
Venue
2021
10.1109/TVCG.2020.3030467
IEEE Transactions on Visualization and Computer Graphics
Keywords
DocType
Volume
Storyline visualization,reinforcement learning,mixed-initiative design
Journal
27
Issue
ISSN
Citations 
2
1077-2626
8
PageRank 
References 
Authors
0.38
27
10
Name
Order
Citations
PageRank
Tan Tang1344.36
Renzhong Li280.38
Xinke Wu37211.15
Shuhan Liu480.38
Johannes Knittel5163.85
Steffen Koch634126.58
Thomas Ertl74417401.52
Lingyun Yu85511.26
Peiran Ren980.72
Yingcai Wu10122361.26