Demo Data from EDBT 2026 / reviewers in the wild / expert
Walter Meiss
dblp:272/7439
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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 · 50% Computer animation and physical simulation · 50% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › virtual reality
virtual reality applications |
0.4 | 1 | 2020 | Exertion-aware path generation · ACM Trans. Graph. 2020 |
Methods — techniques the papers use, named apart from their topics
optimization · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Exertion-aware path generationabstractWe propose a novel approach for generating paths with desired exertion properties, which can be used for delivering highly realistic and immersive virtual reality applications that help users achieve exertion goals. Given a terrain as input, our optimization-based approach automatically generates feasible paths on the terrain which users can bike to perform body training in virtual reality. The approach considers exertion properties such as the total work and the perceived level of path difficulty in generating the paths. To verify our approach, we applied it to generate paths on a variety of terrains with different exertion targets and constraints. To conduct our user studies, we built an exercise bike whose force feedback was controlled by the elevation angle of the generated path over the terrain. Our user study results showed that users found exercising with our generated paths in virtual reality more enjoyable compared to traditional exercising approaches. Their energy expenditure in biking the generated paths also matched with the specified targets, validating the efficacy of our approach. Wanwan Li, Biao Xie, Walter Meiss, Haikun Huang, Lap-Fai Yu |
ACM Trans. Graph. | 4 |