The clutch: two-handed mobile multi-touch 3D object translation and manipulation
Bibliographic record
Abstract
Nowadays, handheld devices such as smartphones provide users with multi-touch input screens. Displaying interactive and touch-enabled 3D environments in such handheld devices has become popular in different applications like games or virtual reality. Technologies such as Web3D and WebGL have made the creation and display of 3D environments in mobile devices easier than ever. However, object manipulation techniques are not as well developed. For example, moving an object within the 3D environment or other similar object-specific manipulations are neither intuitive nor easy to perform. Current manipulation techniques like Gizmo that are successful in systems that use mouse and keyboard are not designed for and do not work well for multi-touch handheld devices. In this paper, we present a novel technique to perform object manipulation in 6DOF in multi-touch screens. Our performance evaluations show that our technique compared to existing techniques such as Gizmo improves task completion time by 63% while increasing task precision by 52%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".