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Record W4409761094 · doi:10.1109/vrw66409.2025.00083

A Novel Bare-Handed Manipulation Technique for Distant Objects in Virtual Reality

2025· article· en· W4409761094 on OpenAlexaff
Di Zhao, Wolfgang Stuerzlinger

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsVirtual realityComputer scienceHuman–computer interactionComputer graphics (images)Mixed realityComputer vision

Abstract

fetched live from OpenAlex

Manipulating distant 3D objects in virtual reality (VR) with bare hands remains a challenge despite advancements in hand-tracking technology. We introduce the Two-Hand Fingertip-Palm (THFP) technique, which extends the interactive zone to the entire non-dominant hand to manipulate distant objects precisely. A user study comparing THFP to a bare-hand variant of HOMER (BHOMER) revealed that THFP achieves 75% greater accuracy, particularly in complex tasks like hanging a painting on a slanted wall, though it is 47% slower. THFP also received higher usability ratings (SUS) and reduced cognitive and physical demands (NASA-TLX), highlighting its user-centric design. These findings demonstrate THFP’s potential for improving distant object manipulation in 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.026
GPT teacher head0.273
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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