StripBrush: A Constraint-Relaxed 3D Brush Reduces Physical Effort and\n Enhances the Quality of Spatial Drawing
Bibliographic record
Abstract
Spatial drawing using ruled-surface brush strokes is a popular mode of\ncontent creation in immersive VR, yet little is known about the usability of\nexisting spatial drawing interfaces or potential improvements. We address these\nquestions in a three-phase study. (1) Our exploratory need-finding study (N=8)\nindicates that popular spatial brushes require users to perform large wrist\nmotions, causing physical strain. We speculate that this is partly due to\nconstraining users to align their 3D controllers with their intended stroke\nnormal orientation. (2) We designed and implemented a new brush interface that\nsignificantly reduces the physical effort and wrist motion involved in VR\ndrawing, with the additional benefit of increasing drawing accuracy. We achieve\nthis by relaxing the normal alignment constraints, allowing users to control\nstroke rulings, and estimating normals from them instead. (3) Our comparative\nevaluation of StripBrush (N=17) against the traditional brush shows that\nStripBrush requires significantly less physical effort and allows users to more\naccurately depict their intended shapes while offering competitive ease-of-use\nand speed.\n
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".