Bibliographic record
Abstract
The postures a character adopts over time are a key expressive aspect of her movement. While IK tools help a character achieve positioning constraints, there are few tools that help an animator with the expressive aspects of a character's poses. Three aspects are combined in good pose design: achieving a set of world space constraints, finding a body shape that reflects the character's inner state and personality, and making adjustments to balance that act to strengthen the pose and also maintain realism. This is routinely done in the performing arts, but is uncommon in computer graphics. Our system combines all three components within a single body shape solver. The system combines feedback based balance control with a hybrid IK system that utilizes optimization and analytic IK components. The IK system has been carefully designed to allow direct control over various aesthetically important aspects of body shape, such as the type of curve in the spine and the relationship between the collar bones. The system allows for both low-level control and for higher level shape sets to be defined and used. Shape sets allow an animator to use a single scalar to vary a character's pose within a specified shape class, providing an intuitive parameterization of a posture. Changing shape sets allows an animator to quickly experiment with different posture options for a movement sequence, supporting rapid exploration of the aesthetic space.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".