MétaCan
Menu
Back to cohort
Record W4225115573 · doi:10.1145/3491101.3519867

Terrain Modelling with a Pen & Touch Tablet and Mid-Air Gestures in Virtual Reality

2022· article· en· W4225115573 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCHI Conference on Human Factors in Computing Systems Extended Abstracts · 2022
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGestureHuman–computer interactionComputer scienceVirtual realityModalitiesTask (project management)TerrainComputer graphics (images)MultimediaArtificial intelligenceEngineering

Abstract

fetched live from OpenAlex

World building or terrain modelling is an essential task when designing games, natural simulations or artistic creations involving virtual 3D landscapes. To support this task, we propose a virtual reality (VR) system based on a pen and touch tablet used in a sitting position (desktop VR) such that both hands are free to interact in an asymmetric way (pen hand + other bare hand). We present and compare several techniques to perform navigation, sculpting and menu operations using the two hands, which interact on and above the tablet surface, i.e. using the pen, touch and mid-air input spaces. A qualitative evaluation with 16 participants confirms the entertaining nature and practical benefits of our system. The study further underlines the complementarity of the different modalities and identifies the promising—and as of yet underexplored—combination of bimanual touch + pen + mid-air interaction in desktop VR.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.313
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it