F2故事:一个模块化框架,用于多目标优化故事情节,重点是公平
IEEE transactions on visualization and computer graphics
|November 20, 2025
概括
传统的故事情节可视化可以在各组中偏向角色可读性. F2Stories是一个使用混合整数线性编程 (MILP) 的新框架,为平衡,可读可读的可视化提供公平性和焦点模式.
科学领域:
- 计算机科学 计算机科学
- 数据可视化 数据可视化
- 人与计算机的交互
背景情况:
- 故事情节可视化描绘了随着时间的推移角色的互动.
- 优化全球可读性指标可以产生偏见,使少数群体更难跟踪.
- 现有的方法难以平衡群体可读性与叙事结构.
研究的目的:
- 开发一个框架,在不同的角色群体中实现平衡的故事情节可视化.
- 为少数群体或代表性较低的群体解决可读性方面的偏见.
- 为公平性,焦点和标准可读性提供灵活的优化模式.
主要方法:
- 推出了F2Stories,这是一个用于故事情节可视化的模块化框架.
- 使用混合整数线性编程 (MILP) 来进行优化.
- 实现了三个模式:公平模式,焦点模式和标准模式.
主要成果:
- F2Stories能够实现更公平的故事情节可视化,在需要时优先考虑特定群体.
- 该框架保持了叙事结构,同时提高了跨群体的可读性.
- 实验分析和案例研究表明,在现实世界中有效.
结论:
- F2Stories为多组故事情节可视化提供了一种新的,优化的方法.
- 基于MILP的方法提供最佳性保证和灵活性.
- 这个框架增强了复杂的角色动态的视觉表现.
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