MétaCan
Menu
Back to cohort
Record W2040842605 · doi:10.1145/1174429.1174467

An interface for virtual 3D sculpting via physical proxy

2006· article· en· W2040842605 on OpenAlexaff
Sheng Jia, Ravin Balakrishnan, Karan Singh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceGestureSmoothingInterface (matter)Human–computer interactionComputer graphics (images)Frame (networking)Virtual realityVirtual spaceSet (abstract data type)Computer vision

Abstract

fetched live from OpenAlex

We explore the design space of using direct finger input in conjunction with a deformable physical prop for the creation and manipulation of conceptual 3D geometric models. The user sculpts virtual models by manipulating the space on, into, and around the physical prop, in an extension of the types of manipulations one would perform on traditional modeling media such as clay or foam. The prop acts as a proxy to the virtual model and hence as a frame of reference to the user's fingers. A prototype implementation uses camera-based motion tracking technology to track passive markers on the fingers and prop. The interface supports a variety of clay-like sculpting operations including deforming, smoothing, pasting, and extruding. All operations are performed using the unconstrained fingers, with command input enabled by a small set of finger gestures coupled with on-screen widgets.

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)0.002
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 score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0150.002

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 head0.008
GPT teacher head0.284
Teacher spread0.276 · 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

Citations83
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicInteractive and Immersive DisplaysFrench-language works237,207