制作数据驱动的图表动画

Yuancheng Shen, Yue Zhao, Yunhai Wang, Tong Ge, Haoyan Shi, Bongshin Lee
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摘要

我们介绍了一种名为 CAST+ (Canis Studio Plus)的创作工具,它可以通过直接操作关键帧来交互式创建图表动画。它引入了图表动画的可视化规范,包括可连续或同时播放的关键帧和动画参数(如持续时间、延迟)。CAST+ 基于 Canis [1](一种利用数据丰富的 SVG 图表的声明式图表动画语法),支持自动完成关键帧和关键帧序列的构建。它还能让用户通过直接操作来完善动画规范(例如,跨轨道对齐关键帧以一起播放,调整延迟)。我们报告了一项用户研究,目的是评估可视化规范和初始版本系统的可用性。我们增强了系统的表现力和可用性:CAST+ 现在支持在同一关键帧组中使用多种类型的视觉标记动画,并采用了基于广义选择的新自动完成算法。这样就能创建更具表现力的动画,同时减少创建类似动画所需的交互次数。我们介绍了一系列示例和四个使用场景,以展示 CAST+ 的表现力。最后,我们讨论了 CAST+ 的局限性、比较和潜力,以及未来的研究方向。
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Authoring Data-Driven Chart Animations.

We present an authoring tool, called CAST+ (Canis Studio Plus), that enables the interactive creation of chart animations through the direct manipulation of keyframes. It introduces the visual specification of chart animations consisting of keyframes that can be played sequentially or simultaneously, and animation parameters (e.g., duration, delay). Building on Canis [1], a declarative chart animation grammar that leverages data-enriched SVG charts, CAST+ supports auto-completion for constructing both keyframes and keyframe sequences. It also enables users to refine the animation specification (e.g., aligning keyframes across tracks to play them together, adjusting delay) with direct manipulation. We report a user study conducted to assess the visual specification and system usability with its initial version. We enhanced the system's expressiveness and usability: CAST+ now supports the animation of multiple types of visual marks in the same keyframe group with new auto-completion algorithms based on generalized selection. This enables the creation of more expressive animations, while reducing the number of interactions needed to create comparable animations. We present a gallery of examples and four usage scenarios to demonstrate the expressiveness of CAST+. Finally, we discuss the limitations, comparison, and potentials of CAST+ as well as directions for future research.

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