RouteFlow: Trajectory-Aware Animated Transitions

要旨

Animating objects’ movements is widely used to facilitate tracking changes and observing both the global trend and local hotspots where objects converge or diverge. Existing methods, however, often obscure critical local hotspots by only considering the start and end positions of objects' trajectories. To address this gap, we propose RouteFlow, a trajectory-aware animated transition method that effectively balances the global trend and local hotspots while minimizing occlusion. RouteFlow is inspired by a real-world bus route analogy: objects are regarded as passengers traveling together, with local hotspots representing bus stops where these passengers get on and off. Based on this analogy, animation paths are generated like bus routes, with the object layout generated similarly to seat allocation according to their destinations. Compared with state-of-the-art methods, RouteFlow better facilitates identifying the global trend and locating local hotspots while performing comparably in tracking objects' movements.

受賞
Best Paper
著者
Duan Li
Tsinghua University, Beijing, China
Xinyuan Guo
Tsinghua University, Beijing, China
Xinhuan Shu
Newcastle University, Newcastle Upon Tyne, United Kingdom
Lanxi Xiao
Tsinghua University, Beijing, China
Lingyun Yu
Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, China
Shixia Liu
Tsinghua University, Beijing, China
DOI

10.1145/3706598.3714300

論文URL

https://dl.acm.org/doi/10.1145/3706598.3714300

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Understanding and Working with Algorithms

Annex Hall F206
6 件の発表
2025-04-29 23:10:00
2025-04-30 00:40:00
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