Expert-Driven Perceptual Features for Modeling Style and Affect in Human Motion
Bibliographic record
Abstract
This paper presents a novel approach for modeling features of style and affect in human motion. Our approach is based on inputs collected from experienced animators. For this purpose, an interface is developed that allows for editing of motion sequences by adding a limited number of Gaussian radial basis functions (RBFs) to different joint trajectories in 3-D Cartesian space. Animators are asked to alter a neutral walking sequence to synthesize happy, sad, feminine, masculine, energetic, and tired variants. Through consolidating the sets of collected RBFs, we compute an expert-driven set of features that can transform neutral walks to the mentioned variations. Moreover, details regarding the use of posture versus movement features, the most frequently edited body joints, as well as shapes, intensities, and distributions of the edits are investigated and presented. Perception feedback from a group of nonexperts validates the proposed approach and the effectiveness, efficiency, scalability, and inversion of the proposed models. The perception study also sheds light on several aspects of perceiving style and affect from motion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".