Kinodynamic skinning using volume-preserving deformations
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
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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