ProInterAR: A Visual Programming Platform for Creating Immersive AR Interactions
Bibliographic record
Abstract
AR applications commonly contain diverse interactions among different AR contents. Creating such applications requires creators to have advanced programming skills for scripting interactive behaviors of AR contents, repeated transferring and adjustment of virtual contents from virtual to physical scenes, testing by traversing between desktop interfaces and target AR scenes, and digitalizing AR contents. Existing immersive tools for prototyping/authoring such interactions are tailored for domain-specific applications. To support programming general interactive behaviors of real object(s)/environment(s) and virtual object(s)/environment(s) for novice AR creators, we propose ProInterAR, an integrated visual programming platform to create immersive AR applications with a tablet and an AR-HMD. Users can construct interaction scenes by creating virtual contents and augmenting real contents from the view of an AR-HMD, script interactive behaviors by stacking blocks from a tablet UI, and then execute and control the interactions in the AR scene. We showcase a wide range of AR application scenarios enabled by ProInterAR, including AR game, AR teaching, sequential animation, AR information visualization, etc. Two usability studies validate that novice AR creators can easily program various desired AR applications using ProInterAR.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".