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Record W2146099949 · doi:10.5555/1632592.1632612

Staggered poses: a character motion representation for detail-preserving editing of pose and coordinated timing

2008· article· en· W2146099949 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceRepresentation (politics)Motion (physics)Motion captureArtificial intelligenceComputer visionCharacter (mathematics)Feature (linguistics)WorkflowMovement (music)Maxima and minimaMathematicsDatabase

Abstract

fetched live from OpenAlex

We introduce staggered poses—a representation of character motion that explicitly encodes coordinated timing among movement features in different parts of a character’s body. This representation allows us to provide sparse, pose–based controls for editing motion that preserve existing movement detail, and we describe how to edit coordinated timing among extrema in these controls for stylistic editing. The staggered pose representation supports the editing of new motion by generalizing keyframe–based workflows to retain high–level control after local timing and transition splines have been created. For densely–sampled motion such as motion capture data, we present an algorithm that creates a staggered pose representation by locating coordinated movement features and modeling motion detail using splines and displacement maps. These techniques, taken together, enable feature–based keyframe editing of dense motion data. Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Computer Graphics]: Animation 1.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.048
GPT teacher head0.256
Teacher spread0.208 · 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

Quick stats

Citations20
Published2008
Admission routes1
Has abstractyes

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