Integrating Continuous and Teleporting VR Locomotion into a Seamless ‘HyperJump’ Paradigm
Bibliographic record
Abstract
Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration . However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness. However, teleportation cannot support continuous spatial updating and can increase disorientation. Thus, we designed 'HyperJump' in an attempt to merge benefits from continuous locomotion and teleportation. HyperJump adds iterative jumps every half a second on top of the continuous movement and was hypothesized to facilitate faster travel without compromising spatial awareness/orientation. In a user study, Participants travelled around a naturalistic virtual city with and without HyperJump (equivalent maximum speed). They followed waypoints to new landmarks, stopped near them and pointed back to all previously visited landmarks in random order. HyperJump was added to two continuous locomotion interfaces (controller- and leaning-based). Participants had better spatial awareness/orientation with leaning-based interfaces compared to controller-based (assessed via rapid pointing). With HyperJump, participants travelled significantly faster, while staying on the desired course without impairing their spatial knowledge. This provides evidence that optical flow can be effectively limited such that it facilitates faster travel without compromising spatial orientation. In future design iterations, we plan to utilize audio-visual effects to support jumping metaphors that help users better anticipate and interpret jumps, and use much larger virtual environments requiring faster speeds, where cybersickness will become increasingly prevalent and thus teleporting will become more important.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".