A Visual-Inertial Hybrid Controller Approach to Improving Immersion in 3D Video Games
Bibliographic record
Abstract
Advances in various areas such as graphics, sound, physics and artificial intelligence have improved the level of player immersion into the gaming environment significantly over the years. However, current game controller systems do not fully facilitate natural bodily motions in an accurate and responsive manner, which may affect player immersion within a gaming environment. This paper presents a novel visual-inertial approach that can potentially improve immersion in 3D video games. The proposed game controller system utilizes visual sensors (i.e. cameras) and inertial sensors (i.e. accelerometers and gyro-sensors) in a synergistic fashion to provide better 3D spatial positioning information than either of the individual technologies can provide. As a result, the proposed game controller system facilitates highly accurate, responsive, and natural control over 3D environments. This makes it well suited for potentially improving player immersion in future 3D video games. Furthermore, several applications of the proposed game controller system are presented to illustrate its potential for improving immersion in 3D video games.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".