Users' head movement obtained from avatars in social VR applications
Bibliographic record
Abstract
Virtual Reality has been endorsed as a highly beneficial tool for face and hand-head gesture research [Sidenmark and Gellersen (2019), ACM Trans. Computer-Human Interaction 27(4)]. Studies have shown [i.e., Xu, X. et al., J. Biomech. 48(4), 721–724 (2015)] that avatars accurately simulate user movements and allow researchers to obtain users' movements remotely without in-person contact or external recordings taken by the users. This study examines head movements of avatars in three popular social VR applications (Altspace, Oculus Home, and vTime XR) using Openface 2.0 [Baltrusaitis et al., in 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018) (2018), pp. 59–66], software that measures head movements in a video. In each of these three platforms, we recorded a user making a sequence of head movements, rotating side-to-side and up-and-down to their maximum extension. Pilot results show that avatar movements in all three apps could be tracked by OpenFace 2.0. Further analysis will include which application's avatars best represent the user's real-time movements. This study provides insight into speech behaviour research and whether user movements are analysable via an avatar, without in-person contact or lab facilities under the new COVID-19 regulations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".