MétaCan
Menu
Back to cohort
Record W2405022506 · doi:10.1145/2858036.2858138

ChronoFab

2016· article· en· W2405022506 on OpenAlexaff
Rubaiat Habib Kazi, Tovi Grossman, Cory Mogk, Ryan Schmidt, George Fitzmaurice

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsAutodesk (Canada)
Fundersnot available
KeywordsAnimationComputer scienceComputer graphics (images)Motion (physics)Computer animationDepictionSculptureObject (grammar)Artificial intelligenceComputer visionArtVisual arts

Abstract

fetched live from OpenAlex

We present ChronoFab, a 3D modeling tool to craft motion sculptures, tangible representations of 3D animated models, visualizing an object's motion with static, transient, ephemeral visuals that are left behind. Our tool casts 3D modeling as a dynamic art-form by employing 3D animation and dynamic simulation for the modeling of motion sculptures. Our work is inspired by the rich history of stylized motion depiction techniques in existing 3D motion sculptures and 2D comic art. Based on a survey of such techniques, we present an interface that enables users to rapidly explore and craft a variety of static 3D motion depiction techniques, including motion lines, multiple stroboscopic stamps, sweeps and particle systems, using a 3D animated object as input. In a set of professional and non-professional usage sessions, ChronoFab was found to be a superior tool for the authoring of motion sculptures, compared to traditional 3D modeling workflows, reducing task completion times by 79%.

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)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.145
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.1450.036

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 head0.005
GPT teacher head0.162
Teacher spread0.157 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicHuman Motion and AnimationFrench-language works237,207