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Xiyu Feng

dblp:247/4069 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · unresolved

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

Computer networks · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 1

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
Virtual and augmented reality · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › immersive interaction › gaze-based interaction
hands-free interaction
0.412019
DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems · VR 2019
Virtual and augmented reality
immersive interaction
0.412019
DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems · VR 2019
Virtual and augmented reality › immersive interaction
text input
0.412019
DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems · VR 2019
Virtual and augmented reality › virtual reality
mobile virtual reality
0.112019
DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems · VR 2019

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

longitudinal study · 0.4IMU sensing · 0.4
YearPublicationVenuePosition
2026 High-Accuracy Ranging Scheme for ISAC-OFDM Systems with Adaptive Chirp-Z Transform
Dongjian Li, Xiyu Feng, Xuhui Ding, Anqi Meng
WCNC4
2019 DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems
abstract
Text entry is a common activity in virtual reality (VR) systems. However, there is a limited number of approaches available for mobile VR systems, where it might be inconvenient for users to carry an input device. We propose a novel hands-free text entry technique we call DepthText which leverages the acceleration sensing abilities of built-in IMU sensors of mobile VR systems. Users are able to enter text by moving their head forward. The results of a 5-day study indicate that users can achieve an average of 10.76 words per minute (wpm) on the last day with low errors. This performance is comparable to the dwell-based technique which is the most common way of entering text that is hands-free. One advantage of DepthText over the dwell-based technique is that users can have more control of the pace of selecting characters, rather than being pushed by a pre-set dwell time.
Xueshi Lu, Difeng Yu, Hai-Ning Liang, Xiyu Feng, Wenge Xu
VR4