Kinodynamic skinning using volume-preserving deformations
Bibliographic record
Abstract
We present a new approach to character skinning where divergence-free vector fields induced by skeletal motion, describe the velocity of skin deformation. The joint transformations for a pose relative to a rest pose create a bend deformation field, resulting in pose-dependent or kinematic skin deformations, varying smoothly across joints. The bend deformation parameters are interactively controlled to capture the varying deformability of bone and other anatomic tissue within an overall fold-over free and volume-preserving skin deformation. Subsequently, we represent the dynamics of skeletal motion, tissue elasticity, muscular tension and the environment as forces that are mapped to vortices at tissue interfaces. A simplified Biot-Savart law in the context of elastic deformation recovers a divergence-free velocity field from the vorticity. Finally, we apply a new stable technique to efficiently integrate points along their deformation trajectories. Adding these dynamic forces over a window of time prior to a given pose provides a continuum of user controllable kinodynamic skinning. A comprehensive implementation using a typical animator workflow in Maya shows our approach to be effective for complex character skinning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".