Pinging Between Worlds: Training Table Tennis Novice Players in Real Environment for Virtual Reality Competitions
Bibliographic record
Abstract
Modern Virtual Reality (VR) technology has enabled users to experience Real Environment (RE) sports in their homes. For VR table tennis, one of the most popular VR sports, the players have rankings and tournaments and compete for RE awards. Based on this phenomenon, this paper aims to understand the benefits of RE training in improving VR table tennis skills. In a user study with 12 novice table tennis players, we measured their performance in 16 basic skills via a pre- and post-study design using a novel training protocol designed for both RE and VR players. We also asked participants for their insights into the training and to evaluate their experience. Our results show a significant improvement in all measured skills. However, participants identified issues with the technology that caused discomfort. Our findings provide valuable insights for software developers working on VR sports applications, enabling them to create better experiences for VR table tennis players. They can also help developers of VR training applications identify areas for improvement with the current technology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".