Yuan Chen

selected publications

1. GI

   Exploring the Effects of Intended Use on Targeting in Virtual Reality

   Chen, Yuan, Casiez, Géry, Malacria, Sylvain, and Lank, Ed

   Graphics Interface 2023 2023

   Url
2. INTERACT

   Empirical evaluation of moving target selection in virtual reality using egocentric metaphors

   Chen, Yuan, Sun, Junwei, Xu, Qiang, Lank, Edward, Irani, Pourang, and Li, Wei

   IFIP Conference on Human-Computer Interaction 2021

   Url
3. INTERACT

   Global Scene Filtering, Exploration, and Pointing in Occluded Virtual Space

   Chen, Yuan, Sun, Junwei, Xu, Qiang, Lank, Edward, Irani, Pourang, and Li, Wei

   IFIP Conference on Human-Computer Interaction 2021

   Url
4. CHI

   Understanding Viewport- and World-Based Pointing with Everyday Smart Devices in Immersive Augmented Reality

   Chen, Yuan, Katsuragawa, Keiko, and Lank, Edward

   Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 2020

   Abs Url Video

   Personal smart devices have demonstrated a variety of efficient techniques for pointing and selecting on physical displays. However, when migrating these input techniques to augmented reality, it is both unclear what the relative performance of different techniques will be given the immersive nature of the environment, and it is unclear how viewport-based versus world-based pointing methods will impact performance. To better understand the impact of device and viewing perspectives on pointing in augmented reality, we present the results of two controlled experiments comparing pointing conditions that leverage various smartphone- and smartwatch-based external display pointing techniques and examine viewport-based versus world-based target acquisition paradigms. Our results demonstrate that viewport-based techniques offer faster selection and that both smartwatch- and smartphone-based pointing techniques represent high-performance options for performing distant target acquisition tasks in augmented reality.

5. VIS

   SkyLens: Visual Analysis of Skyline on Multi-Dimensional Data

   Zhao, X., Wu, Y., Cui, W., Du, X., Chen, Y., Wang, Y., Lee, D. L., and Qu, H.

   IEEE Transactions on Visualization and Computer Graphics 2018

   Abs Url

   Skyline queries have wide-ranging applications in fields that involve multi-criteria decision making, including tourism, retail industry, and human resources. By automatically removing incompetent candidates, skyline queries allow users to focus on a subset of superior data items (i.e., the skyline), thus reducing the decision-making overhead. However, users are still required to interpret and compare these superior items manually before making a successful choice. This task is challenging because of two issues. First, people usually have fuzzy, unstable, and inconsistent preferences when presented with multiple candidates. Second, skyline queries do not reveal the reasons for the superiority of certain skyline points in a multi-dimensional space. To address these issues, we propose SkyLens, a visual analytic system aiming at revealing the superiority of skyline points from different perspectives and at different scales to aid users in their decision making. Two scenarios demonstrate the usefulness of SkyLens on two datasets with a dozen of attributes. A qualitative study is also conducted to show that users can efficiently accomplish skyline understanding and comparison tasks with SkyLens.