Navigation interfaces for virtual reality and gaming: Theory and practice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this course, we will take a detailed look at various breeds of spatial navigation interfaces that allow for locomotion in digital 3D environments such as games, virtual environments or even the exploration of abstract data sets. We will closely look into the basics of navigation, unraveling the psychophysics (including wayfinding) and actual navigation (travel) aspects. The theoretical foundations form the basis for the practical skillset we will develop, by providing an in-depth discussion of navigation devices and techniques, and a step-by-step discussion of multiple real-world case studies. Doing so, we will cover the full range of navigation techniques from handheld to full-body, highly engaging and partly unconventional methods and tackle spatial navigation with hands-on-experience and tips for design and validation of novel interfaces. In particular, we will be looking at affordable setups and ways to “trick” out users to enable a realistic feeling of self-motion in the explored environments. As such, the course unites the theory and practice of spatial navigation, serving as entry point to understand and improve upon currently existing methods for the application domain at hand.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it