Bibliographic record
Abstract
Our progress in the problem of making animated characters move expressively has been slow, and it persists in being among the most challenging in computer graphics. Simply attending to the low-level motion control problem, particularly for physically based models, is very difficult. Providing an animator with the tools to imbue character motion with broad expressive qualities is even more ambitious, but it is clear it is a goal to which we must aspire. Part of the problem is simply finding the right language in which to express qualities of motion. Another important issue is that expressive animation often involves many disparate parts of the body, which thwarts bottom-up controller synthesis. We demonstrate progress in this direction through the specification of directed, expressive animation over a limited range of standing movements. A key contribution is that through the use of high-level concepts such as character sketches, actions and properties, which impose different modalities of character behaviour, we are able to create many different animated interpretations of the same script. These tools support both rapid exploration of the aesthetic space and detailed refinement. Basic character actions and properties are distilled from an extensive search in the performing arts literature. We demonstrate how all high-level constructions for expressive animation can be given a precise semantics that translate into a low-level motion specification that is then simulated either physically or kinematically. Our language and system can act as a bridge across artistic and technical communities to resolve ambiguities regarding the language of motion. We demonstrate our results through an implementation and various examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.235 |
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".