Modeling tension and relaxation for computer animation
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
The use of tension and relaxation in the muscles of real creatures gives rise to nuanced motion that conveys emotion or intent. Artists have long exploited knowledge of this in traditional animation and other areas, but it has been both overlooked and difficult to achieve in physically based animation. The robotically stiff motion that has come to typify physically based approaches belies the fact that dynamics has much to offer in facilitating far more subtle motion in which animators could freely "shape" a motion. We demonstrate that tension and relaxation can be introduced into joint-level, posture based animation. While we show that these modalities can be efficiently incorporated into traditional proportional-derivative control models, we instead formulate a more flexible and better-behaved model based on antagonistic control. This approach is more biomechanically sound, but more importantly it permits the separation of stiffness control from position control, achieving better posture interpolation, better error control, and passive and active dynamics. We introduce effective mechanisms to control the shape of a motion and describe an animation system that efficiently integrates relaxation and tension control in a physically based simulation environment.
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 | 0.000 |
| 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