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Record W2700358705

Expressive motion editing using motion extrema

2012· dissertation· en· W2700358705 on OpenAlexaff
Karan Singh, Patrick Coleman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMotion captureMotion (physics)Computer scienceMaxima and minimaArtificial intelligenceCharacter animationSet (abstract data type)Computer visionAnimationHuman–computer interactionComputer graphics (images)Computer animationMathematicsProgramming language
DOInot available

Abstract

fetched live from OpenAlex

When animating characters, a key goal is the creation of a believable, expressive performance that gives a character a unique personality with a distinct style of movement. While animators are skilled at creating expressive, personalized performances, it remains challenging to change performance-related aspects of movement in existing motion data. In recent years, motion data reuse has become increasingly important as recorded motion capture data has come into widespread use. This thesis investigates the use of a sparse set of pose-centric editing controls for editing existing motion data using techniques similar to those used by keyframe animators when they create new motion. To do this, this thesis proposes the use of motion extrema—the poses a character passes through when there is a significant change in movement—as a means for choosing effective pose-centric editing controls. First, I present algorithms for identifying motion extrema. Motion extrema can be associated with individual joints or the full body of the character. I introduce a set of approaches for identifying motion extrema; these include the use of extrema of differential measures and the explicit search for times at which the body or a joint is in a spatially extreme configuration. I then present three motion editing applications that use motion extrema as a foundation for applying motion edits. The first application, pose-centric editing, allows users to interactively change poses in a motion, and the system modifies the motion to respect existing ground contact. The second application—staggered poses, introduces a model of character pose that explicitly encodes how timing varies among motion extrema on different parts of the body. This timing variation is commonly used by animators to model overlapping action. By introducing an algorithm for finding timing variation on motion extrema in existing motion, this system enables users to make high-level changes to timing patterns to change overlap effects in existing motion. Finally, I present a procedural motion editing application that targets a specific aspect of motion style; this technique is called spatial exaggeration. Spatial exaggeration changes the geometric relationships among extreme poses. Such edits cause movement to appear more or less energetic. Overall, these applications demonstrate that performance-related aspects of existing motion can be edited using a sparse set of controls in the form of motion extrema.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.256
Teacher spread0.230 · 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
Published2012
Admission routes1
Has abstractyes

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