Controlling an avatar's pointing gestures in desktop collaborative virtual environments
Bibliographic record
Abstract
Collaborative Virtual Environments (CVEs) allow people to interact with each other in virtual worlds through computer-generated avatars. Avatars are much less expressive than real bodies, and one main limitation is their lack of support for non-verbal communication such as pointing gestures. Part of the problem is that these gestures must be created through an input device, but the user is already busy controlling the avatar's location, rotation, and view direction. Pointing gestures are only useful for collaborative communication if they can be controlled simultaneously with all other avatar actions. To determine whether there are input configurations that make pointing gestures feasible, we carried out a study that compared five different widely-available input devices in three non-verbal communication tasks. We found that users were able to successfully incorporate pointing gestures into tasks that already involved moving, turning, and looking, but that there were significant and substantial differences between devices. Two configurations performed best: a mode-switched version of standard mouse-and-keyboard control, and a direct-pointing scheme using a Wii remote. There were also minor effects of gender and video-game experience. Our study suggests that users will be able to successfully create free pointing gestures in CVEs, greatly improving the communicative richness of these environments.
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.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".