EDBT 2026 Demo / reviewers in the wild / expert
Shuai Luan
dblp:92/7566
· DBLP profile ↗
3ranked-venue papers
0as first author
3since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 3 · 3 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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
2 papers |
Interaction techniques and input · 36% User interface design and tools · 31% Immersive interaction · 27% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
User interface design and tools › visualization
label layout |
1.0 | 1 | 2026 | User perception based label layout for efficient target localization in virtual environment · Int. J. Hum. Comput. Stud. 2026 |
Immersive interaction
virtual reality interaction |
0.9 | 2 | 2026 | Distant Object Manipulation with Adaptive Gains in Virtual Reality · ISMAR 2022 User perception based label layout for efficient target localization in virtual environment · Int. J. Hum. Comput. Stud. 2026 |
Interaction techniques and input › spatial interaction › 3d interaction
distant object manipulation |
0.6 | 1 | 2022 | Distant Object Manipulation with Adaptive Gains in Virtual Reality · ISMAR 2022 |
Interaction techniques and input
object manipulation |
0.6 | 1 | 2022 | Distant Object Manipulation with Adaptive Gains in Virtual Reality · ISMAR 2022 |
Usability and user experience research
task load |
0.2 | 1 | 2022 | Distant Object Manipulation with Adaptive Gains in Virtual Reality · ISMAR 2022 |
Methods — techniques the papers use, named apart from their topics
user study · 0.6fitting functions · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | User perception based label layout for efficient target localization in virtual environment
Jian Wu 0033, Shuai Luan, Wei Ke 0001, Lili Wang 0006 |
Int. J. Hum. Comput. Stud. | 2 |
| 2024 | Manipulation Guidance Field for Collaborative Object Manipulation in VRabstractObject manipulation is a fundamental interaction in virtual reality (VR). Efficient and accurate manipulation is important for many VR applications, especially collaborative VR applications. We introduce a collaborative method based on the manipulation guidance field (MGF) to improve manipulation accuracy and efficiency. MGF aims to guide users of different manipulation types to different manipulation viewpoints to efficiently and collaboratively manipulate objects. First, we introduce the concept of MGF and its construction method. Two strategies are offered to accelerate the MGF updating process. A collaborative manipulation method to manipulate objects using the guidance of MGF is then proposed. Finally, a user study (n = 36 participants) was conducted to evaluate the efficiency and accuracy of our MGF-based collaborative object manipulation method in three scenes: (1) Livingroom scene; (2) WaveHouse scene; (3) Pipe scene. Compared to a control method without MGF, the results show that our MGF-based method has significantly reduced task completion time, position error, rotation error, and task load. Shuai Luan |
Int. J. Hum. Comput. Interact. | 3 |
| 2022 | Distant Object Manipulation with Adaptive Gains in Virtual RealityabstractObject Manipulation is a fundamental interaction in virtual reality (VR). The efficiency and accuracy of object manipulation are important to provide immersion to users. We propose a manipulation method with adaptive gains to improve the efficiency and accuracy of object manipulation in VR applications. First, we introduce manipulation gains. We then design an experiment to collect user behavior during manipulation to determine fitting functions for calculating manipulation gain. At last, we design a user study to evaluate the performance of our distant object manipulation method with adaptive gains. The results show that, compared with the state of the art methods, our method has a significant improvement in the completion time, and the manipulation accuracy of the tasks. Moreover, our method significantly increases usability and reduces task load. Lili Wang 0006, Shuai Luan, Xuehuai Shi, Xinda Liu |
ISMAR | 3 |