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Fiona Xiao Yu Chen

dblp:374/3513 · DBLP profile ↗
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4ranked-venue papers
2as first author
4since 2021 · last 2026
0009-0009-4041-3233ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 3 · 2 first-author · 3 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 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
2 papers
Virtual and augmented reality · 100%
Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › locomotion
redirected walking
1.822026
Gait-Synced Translation Gain for Naturalistic VR Motion · IEEE Trans. Vis. Comput. Graph. 2026
BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking · IEEE Trans. Vis. Comput. Graph. 2024
Virtual and augmented reality
locomotion
1.012026
Gait-Synced Translation Gain for Naturalistic VR Motion · IEEE Trans. Vis. Comput. Graph. 2026
Virtual and augmented reality › locomotion › redirected walking
rotation gain
0.812024
BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking · IEEE Trans. Vis. Comput. Graph. 2024
Virtual and augmented reality
cybersickness
0.312026
Gait-Synced Translation Gain for Naturalistic VR Motion · IEEE Trans. Vis. Comput. Graph. 2026
Virtual and augmented reality
immersive interaction
0.312026
Gait-Synced Translation Gain for Naturalistic VR Motion · IEEE Trans. Vis. Comput. Graph. 2026
Interaction techniques and input › spatial interaction › navigation
virtual reality navigation
0.212024
BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking · IEEE Trans. Vis. Comput. Graph. 2024

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

user study · 2.5detection threshold measurement · 1.5gait cycle synchronization · 1.0
YearPublicationVenuePosition
2026 Gait-Synced Translation Gain for Naturalistic VR Motion
abstract
Translation gain is a key Redirected Walking (RDW) technique in Virtual Reality (VR) that enables users to navigate virtual environments (VEs) larger than the available physical space. The technique was originally developed to scale users' walking distance in the VE and is typically applied continuously, regardless of the user's motion state. We introduce Gait-Synced Translation Gain (GSTG), a novel approach that adapts translation gain by synchronizing it with the user's gait cycle. GSTG leverages the single-limb support phase of walking-when users are less stable and thus less sensitive to external disturbances-to apply higher levels of gain. This approach allows greater manipulation while preserving natural walking sensations and avoiding additional cybersickness. A user study comparing GSTG with continuous translation gain demonstrates significant improvements in perceived naturalness and comfort. Our results highlight the potential of gait-synchronized gain to enhance immersion, offering new possibilities for more realistic and comfortable VR locomotion.
Fiona Xiao Yu Chen, Sen-Zhe Xu 0001, Kui Huang, Ariel Shamir, Song-Hai Zhang
IEEE Trans. Vis. Comput. Graph.1
2025 Foregrounding collaboration in CAVE systems: A survey across domains, interaction, and system design
Fiona Xiao Yu Chen, Sen-Zhe Xu 0001, Song-Hai Zhang
Comput. Graph.1
2024 Overcoming Spatial Constraints in VR: A Survey of Redirected Walking Techniques
Jia-Hong Liu, Yang-Fu Ren, Qi Wen Gan, Kui Huang, Fiona Xiao Yu Chen, Er-Xia Luo, Khang Yeu Tang, Yue-Yao Fu, Cheng-Wei Fan, Sen-Zhe Xu 0001, Song-Hai Zhang
J. Comput. Sci. Technol.5
2024 BiRD: Using Bidirectional Rotation Gain Differences to Redirect Users during Back-and-forth Head Turns in Walking
abstract
Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.
Sen-Zhe Xu 0001, Fiona Xiao Yu Chen, Song-Hai Zhang
IEEE Trans. Vis. Comput. Graph.2