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Xichen He

dblp:388/8566 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0009-8909-8561ORCID · reported

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

Human-computer interaction and ubiquitous 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.

Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 100%
Computer graphics and multimedia
1 paper
Virtual and augmented reality · 100%

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

TopicWeightPapersLastEvidence papers
Interaction techniques and input › text entry
gesture-based text entry
1.012026
SwEYEpinch and Beyond: Exploring Intuitive, Efficient Text Entry for Extended Reality via Eye and Hand Tracking · CHI 2026
Interaction techniques and input
text entry
1.012026
SwEYEpinch and Beyond: Exploring Intuitive, Efficient Text Entry for Extended Reality via Eye and Hand Tracking · CHI 2026
Virtual and augmented reality › immersive interaction
extended reality interaction
0.312026
SwEYEpinch and Beyond: Exploring Intuitive, Efficient Text Entry for Extended Reality via Eye and Hand Tracking · CHI 2026

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

user study · 2.0dynamic time warping · 2.0
YearPublicationVenuePosition
2026 SwEYEpinch and Beyond: Exploring Intuitive, Efficient Text Entry for Extended Reality via Eye and Hand Tracking
abstract
Despite steady progress, text entry in Extended Reality (XR) often remains slower and more effortful than typing on a physical keyboard or touchscreen. We explore a simple idea: use gaze to swipe through a virtual keyboard for the fast, low-effort where and a manual pinch held throughout the swipe for the when, extending and validating it through a series of user studies. We first show that a basic version including a low-latency decoder with spatiotemporal Dynamic Time Warping and fixation filtering outperforms selecting individual keys sequentially, either by finger tapping each or gazing at each while pinching. We then add mid-swipe prediction and in-gesture cancellation, improving words per minute (WPM) without hurting accuracy. We show that this approach is faster and more preferred than previous gaze-swipe approaches, finger tapping with prediction, or hand swiping with the same additions. Furthermore, a seven-day, 30-session study demonstrates sustained learning, with peak performance reaching 64.7 WPM.
Ziheng 'Leo' Li, Xichen He, Mengyuan Wu, Zeyi Tong, Haowen Wei, Benjamin Yang, Steven K. Feiner, Paul Sajda
CHI2