Demo Data from EDBT 2026 / reviewers in the wild / expert
Tiansu Chen
dblp:354/7317
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0007-7227-7948ORCID · 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 |
Wearable and physiological sensing · 50% Interaction techniques and input · 50% | |
| 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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wearable and physiological sensing › eye tracking
gaze-based interaction |
0.8 | 1 | 2024 | FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation · CHI 2024 |
Interaction techniques and input
hands-free interaction |
0.8 | 1 | 2024 | FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation · CHI 2024 |
Virtual and augmented reality › immersive interaction
3d user interfaces |
0.2 | 1 | 2024 | FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth Manipulation · CHI 2024 |
Methods — techniques the papers use, named apart from their topics
user study · 1.5binocular visual depth detection · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth ManipulationabstractGaze interaction presents a promising avenue in Virtual Reality (VR) due to its intuitive and efficient user experience. Yet, the depth control inherent in our visual system remains underutilized in current methods. In this study, we introduce FocusFlow, a hands-free interaction method that capitalizes on human visual depth perception within the 3D scenes of Virtual Reality. We first develop a binocular visual depth detection algorithm to understand eye input characteristics. We then propose a layer-based user interface and introduce the concept of “Virtual Window” that offers an intuitive and robust gaze-depth VR interaction, despite the constraints of visual depth accuracy and precision spatially at further distances. Finally, to help novice users actively manipulate their visual depth, we propose two learning strategies that use different visual cues to help users master visual depth control. Our user studies on 24 participants demonstrate the usability of our proposed virtual window concept as a gaze-depth interaction method. In addition, our findings reveal that the user experience can be enhanced through an effective learning process with adaptive visual cues, helping users to develop muscle memory for this brand-new input mechanism. We conclude the paper by discussing potential future research topics of gaze-depth interaction. Chenyang Zhang 0002, Tiansu Chen, Eric Shaffer, Elahe Soltanaghai |
CHI | 2 |