Motion perturbation based on simple neuromotor control models
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
Motion capture is widely used for character animation. One of the major challenges of this technique is how to modify the captured motion in plausible ways. Previous work has focused on transformations based on kinematics and dynamics, but has not explicitly taken into account the emerging knowledge of how humans control their movement. In this paper, we show how this can be done using a simple human neuromuscular control model. Our model of muscle forces includes a feedforward term, and low-gain passive feedback. The feedforward component is calculated from motion capture data using inverse dynamics. The feedback component generates reaction forces to unexpected external disturbances. The perturbed animation is then resynthesized using forward dynamics. This allows us to create animation where the character reacts to unexpected external forces in a natural way (e.g., when the character is hit by a flying object), and still retain the quality of the captured motions. This technique is useful for applications such as interactive sports video games.
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