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Record W2150235633 · doi:10.1145/2019406.2019442

Biomechanically-inspired motion path editing

2011· article· en· W2150235633 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
FundersCarnegie Mellon University
KeywordsKinematicsMotion (physics)Path (computing)Computer scienceAccelerationComputer visionProcess (computing)HeuristicsArtificial intelligenceMotion controlTrajectoryCurvatureMotion captureMotion planningMathematicsPhysicsGeometryRobotClassical mechanics

Abstract

fetched live from OpenAlex

We present a system for interactive kinematic editing of motion paths and timing that employs various biomechanical observations to augment and restrict the edited motion. Realistic path manipulations are enforced by restricting user interaction to handles identified along a motion path using motion extrema. An as-rigid-as-possible deformation technique modified specifically for use on motion paths is used to deform the path to satisfy the usermanipulated handle positions. After all motion poses have been adjusted to satisfy the new path, an automatic timewarping step modifies the timing of the new motion to preserve the timing qualities of the original motion. This timewarp is based on biomechanical heuristics relating velocity to stride length and path curvature, as well as the preservation of acceleration for ballistic motion. We show that our system can be used to quickly and easily modify a variety of locomotive motions, and can accurately reproduce recorded motions that were not used during the editing process.

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 categoriesInsufficient payload (model declined to judge)
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 score0.816
Threshold uncertainty score0.999

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)0.0020.000

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 head0.029
GPT teacher head0.197
Teacher spread0.168 · 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

Citations16
Published2011
Admission routes1
Has abstractyes

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