Perceptually Guided Processing of Style and Affect in Human Motion for Multimedia Applications
Bibliographic record
Abstract
Computer graphics and animation, as a direct result of advancements in hardware and software, have become broad and demanding areas of research.Animation of human motion is a major component in the field that has attracted many due to its significance in movies, games, and virtual environments.We propose that processing features for style and affect, which are fundamental determinants of personality and naturally appearing motion, should be carried out through perceptually guided processing techniques.In this dissertation, we employ this approach and develop a set of tools for extraction, synthesis, and analysis of affective and stylistic motion features.Temporal alignment is one of the most common issues in processing motion data.Accordingly, we first propose a new time warping technique for motion.The proposed method outperforms several existing techniques and has advantages such as precise alignment, low distortion, smooth warped motion trajectories, and high customizability.Many motion processing techniques utilize incremental (joint-to-joint) processing of motion sequences.In addition, some systems process only selected joints or regions of the body.Hence, it is imperative to verify whether partial or subsets of computational solutions can lead to perceptually accurate results.Accordingly, we investigate and validate the notion of additivity in perception of affect from motion.A system capable of extracting style/affect features from motion data using spline optimization is then introduced.Our method has several advantages over existing techniques, namely extracting the features as three separate movement, posture, and time components, which are the perceptual and functional sources for stylistic/affective motion.Our method also performs in Cartesian or joint-angle spaces rather than S. A. Etemad and A. Arya, "Perceptually valid motion for avatars,"
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".