• Wenchao Li, Sarah Schöttler, James Scott-Brown, Yun Wang, Siming Chen, Huamin Qu, and Benjamin Bach

• ACM Conference on Human Factors in Computing Systems (CHI), 2023

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This paper introduces semi-automatic data tours to aid the exploration of complex networks. Compared to guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, and subgraphs as seed elements for follow-up tours.

• Wenchao Li, Zhan Wang, Yun Wang, Di Weng, Liwenhan Xie, Siming Chen, Haidong Zhang, and Huamin Qu

• ACM Conference on Human Factors in Computing Systems (CHI), 2023

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This work aims to lower the barrier of crafting diverse camera movements for geographic data videos. We first analyze a corpus of 66 geographic data videos and derive a design space of camera movements with a dimension for geospatial targets and one for narrative purposes. Based on the space, we propose a set of adaptive camera shots and further develop GeoCamera, an interactive tool that empowers users to flexibly design camera movements for geographic visualizations.

• Wenchao Li, Yun Wang, Haidong Zhang, and Huamin Qu

• IEEE Visualization Conference (Short Papers), 2020

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Informed by the role of foreshadowing that builds the expectation in film and literature, we introduce visual foreshadowing to improve the engagement of animated visualizations. In specific, we propose designs of visual foreshadowing that engage the audience while watching the animation. Moreover, we implement a proof-of-concept authoring tool to demonstrate our approach and conduct a user study to learn the efficacy of engagement enhancement.

• Ruizhen Hu, Wenchao Li, Oliver van Kaick, Ariel Shamir, Hao Zhang, and Hui Huang

• ACM Transactions on Graphics (SIGGRAPH Asia), 2017

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We introduce a method for learning a model for the mobility of parts in 3D objects. Our method allows not only to understand the dynamic functionalities of one or more parts in a 3D object, but also to apply the mobility functions to static 3D models.

• Ruizhen Hu, Wenchao Li, Oliver van Kaick, Hui Huang, Melinos Averkiou, Daniel Cohen-Or, and Hao Zhang

• ACM Transactions on Graphics (Presented at SIGGRAPH), 2017

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We introduce a method for co-locating style-defining elements over a set of 3D shapes. Our goal is to translate high-level style descriptions, such as "Ming" or "European" for furniture models, into explicit and localized regions over the geometric models that characterize each style. For each style, the set of style-defining elements is defined as the union of all the elements that are able to discriminate the style. Another property of the style-defining elements is that they are frequently-occurring, reflecting shape characteristics that appear across multiple shapes of the same style.