Bibliographic record
Abstract
The utility of an interactive tool can be measured by how pervasively it is embedded into a user's workflow. Tools for artists additionally must provide an appropriate level of control over expressive aspects of their work while suppressing unwanted intrusions due to details that are, for the moment, unnecessary. Our focus is on tools that target editing the expressive aspects of character motion. These tools allow animators to work in a way that is more expedient than modifying low-level details, and offers finer control than high level, directorial approaches. To illustrate this approach, we present three such tools, one for varying timing (succession), and two for varying motion shape (amplitude and extent). Succession editing allows the animator to vary the activation times of the joints in the motion. Amplitude editing allows the animator to vary the joint ranges covered during a motion. Extent editing allows an animator to vary how fully a character occupies space during a movement -- using space freely or keeping the movement close to his body. We argue that such editing tools can be fully embedded in the workflow of character animators. We present a general animation system in which these and other edits can be defined programmatically. Working in a general pose or keyframe framework, either kinematic or dynamic motion can be generated. This system is extensible to include an arbitrary set of movement edits.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".