Does Perspective Matter? Understanding the Role of Viewpoints on User Performance in 3D Sketching
Bibliographic record
Abstract
Most 3D sketching tools for Virtual Reality (VR) rely on traditional features, like scaling and translating the environment with the hands and viewing the environment using a first-person (1PP) point of view (POV). Yet, VR can enhance the artist’s experience in ways impossible in the physical environment. These novel ways to perceive the sketch might influence users’ behaviours, positively affecting the sketching quality and user experience. In a within-participants user study, we explore the possibilities of using different POVs for sketching by comparing three different perspectives, including first-person POV (1PP), third-person POV (3PP), and multiple third-person POV (M3PP). We collected data on their task performance and subjective experience to evaluate the impact of these perspectives on users’ ability to sketch in VR. Our results reveal that POV notably affects user performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".