Diverse motion variations for physics-based character animation
Why this work is in the frame
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Bibliographic record
Abstract
We present an optimization framework for generating diverse variations of physics-based character motions. This allows for the automatic synthesis of rich variations in style for simulated jumps, flips, and walks. While well-posed motion optimization problems result in a single optimal motion, we explore using underconstrained motion descriptions and then optimizing for diversity. As input, the method takes a parameterized controller for a successful motion instance, a set of constraints that should be preserved, and a pairwise distance metric between motions. An offline optimization then produces a highly diverse set of motion styles for the same task. We demonstrate results for a variety of 2D and 3D physics-based motions and show that this approach can generate compelling new motions.
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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) | 0.001 | 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 it