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Yuancheng Shen

dblp:395/1186 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
0009-0003-3490-6409ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 77% Computer animation and physical simulation · 23%
Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input
direct manipulation
0.912025
Authoring Data-Driven Chart Animations Through Direct Manipulation · IEEE Trans. Vis. Comput. Graph. 2025
Computer animation and physical simulation
data-driven animation
0.312025
Authoring Data-Driven Chart Animations Through Direct Manipulation · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

declarative grammar · 1.7auto-completion algorithm · 1.7
YearPublicationVenuePosition
2025 Authoring Data-Driven Chart Animations Through Direct Manipulation
abstract
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 (Ge et al. 2020), 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.
Yuancheng Shen, Yue Zhao 0033, Yunhai Wang, Tong Ge, Haoyan Shi, Bongshin Lee
IEEE Trans. Vis. Comput. Graph.1