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Record W1964060976 · doi:10.1145/1174429.1174444

3D character animation synthesis from 2D sketches

2006· article· en· W1964060976 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 Waterloo
Fundersnot available
KeywordsAnimationComputer scienceSketchStylized factCharacter animationComputer animationSkeletal animationCharacter (mathematics)Computer graphics (images)Motion captureKey (lock)Computer visionPath (computing)Artificial intelligenceMotion (physics)Matching (statistics)Computer facial animationAlgorithm

Abstract

fetched live from OpenAlex

Traditional character animation has superiority in conveying stylized information about characters and events, but producing it requires a lot of labor and time. Computer generated animation improves greatly on efficiency, but is poor in expressing stylized motion. In this paper, we propose a sketching-based animation synthesis system. The system contains an interface for the user to draw the sketches or load sketch images. Then the system extracts 2D pose from the input strokes and maps the 2D pose to 3D pose that is in a motion capture database. During the mapping, a series of matching 3D pose candidates are found. The user can select the most satisfying candidate as the 3D key pose for the later animation synthesis. The system synthesizes an animation based on the 3D key poses by finding a path in the motion capture database and generating transition motions if needed.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score1.000

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)0.0030.001

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 head0.008
GPT teacher head0.177
Teacher spread0.169 · 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

Citations9
Published2006
Admission routes1
Has abstractyes

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