Inverse kinodynamics: editing and constraining kinematic approximations of dynamic motion
Bibliographic record
Abstract
We present inverse kinodynamics (IKD), an animator friendly kinematic workflow that both encapsulates short-lived dynamics and allows precise space-time constraints. Kinodynamics (KD), defines the system state at any given time as the result of a kinematic state in the recent past, physically simulated over a short temporal window to the present. KD is a well suited kinematic approximation to animated characters and other dynamic systems with dominant kinematic motion and short-lived dynamics. Given a dynamic system, we first choose an appropriate kinodynamic window size based on accelerations in the kinematic trajectory and the physical properties of the system. We then present an inverse kinodynamics (IKD) algorithm, where a kinodynamic system can precisely attain a set of animator constraints at specified times. Our approach solves the IKD problem iteratively, and is able to handle full pose or end effector constraints at both position and velocity level, as well as multiple constraints in close temporal proximity. Our approach can also be used to solve position and velocity constraints on passive systems attached to kinematically driven bodies. We show IKD to be a compelling approach to the direct kinematic control of character, with secondary dynamics via examples of skeletal dynamics and facial animation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".