Extending computational models of abstract motion with movement qualities
Bibliographic record
Abstract
The affectively rich expressive capacity of movement and motion is well established in art, performance, animation and visualization but research in perception, cognitive and social psychology provides only limited insight into the visual features that underpin this richness, and artistic principles are not amenable to computational modeling. Recent research has shown the communicative potential of simple abstract motions, absent of figure, to convey affect [23] based on a limited algorithmic model manipulating basic motion dimensions such as shape, speed and direction. Evidence suggests that descriptive frameworks of human movement expression, such as Laban Movement Analysis (LMA), are effective analytical tools with established principles and models; yet the benefits and challenges of incorporating these concepts into larger frameworks of motion and animation has not been rigorously explored. We present a computational model and prototype implementation that incorporates LMA core concepts and principles with established motion algorithms such that users can represent and explore LMA concepts using abstract motions. The model is the outcome of an indepth qualitative study with Certified Movement Analysts (CMAs) exploring, creating and analyzing the potential of low-level animation features to communicate expressive qualities of movement. A more comprehensive design space includes both new parameters for manipulation and a synthesis of lower-level dimensions into the more semantic concepts of Laban principles. In this paper, we discuss the evolution of the model to incorporate these principles of human movement, next steps, and relate the potential applicability of this research to applications in art, visualization and cognition.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".