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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 it