CAST: Authoring Data-Driven Chart Animations

要旨

We present CAST, an authoring tool that enables the interactive creation of chart animations. 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, 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 and other parameters for animation effects (e.g., animation type, easing function) using a control panel. In addition to describing how CAST infers recommendations for auto-completion, we present a gallery of examples to demonstrate the expressiveness of CAST and a user study to verify its learnability and usability. Finally, we discuss the limitations and potentials of CAST as well as directions for future research.

受賞
Honorable Mention
著者
Tong Ge
Shandong University, Qingdao, China
Bongshin Lee
Microsoft Research, Redmond, Washington, United States
Yunhai Wang
Shandong University, Qingdao, China
DOI

10.1145/3411764.3445452

論文URL

https://doi.org/10.1145/3411764.3445452

動画

会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2021.acm.org/)

セッション: Designing Effective Visualizations

[A] Paper Room 09, 2021-05-13 17:00:00~2021-05-13 19:00:00 / [B] Paper Room 09, 2021-05-14 01:00:00~2021-05-14 03:00:00 / [C] Paper Room 09, 2021-05-14 09:00:00~2021-05-14 11:00:00
Paper Room 09
13 件の発表
2021-05-13 17:00:00
2021-05-13 19:00:00
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