Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Jennifer Roth

dblp:420/7724 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-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
Immersive interaction · 44% Interaction techniques and input · 44% User interface design and tools · 13%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Interaction techniques and input
target selection
0.912025
SliVR: A 360° VR-Hub for Fast Selections in Multiple Virtual Environments · ISMAR 2025

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

user study · 0.9
YearPublicationVenuePosition
2025 SliVR: A 360° VR-Hub for Fast Selections in Multiple Virtual Environments
abstract
Current virtual reality (VR) systems limit users to a single virtual 3D environment (VE) at a time, which restricts their ability to engage with multiple VEs simultaneously. We present SLIVR, a VR-Hub designed to enhance multitasking capabilities by arranging multiple VEs in a regular polygon formation around the user. Each VE is visually represented by life-sized 2D previews that facilitate hub-based interactions. Direct Selection enables users to select objects within a VE directly from the preview, allowing them to remain within the hub. Additionally, Focus-mode enlarges a chosen 2D preview, providing access to objects beyond the initial viewport. In a user study ($\mathrm{N}=18$), we evaluated SLIVR against a conventional grid-based VR app launcher and a hub that utilized SLIVR's visualization but only supported Teleportation interactions. Our findings revealed that SLIVR improved target selection performance by 16.8% overall and by 30.5% when engaging with six VEs, compared to the grid-based app launcher. Notably, hub-based interactions accounted for 94.9% of selections, with Focus-mode being utilized six times more frequently than Teleportation.
Kevin Linne, Sven Thomas 0002, Jennifer Roth, Martin Weigel 0001
ISMAR3