Synthetic motion capture for interactive virtual worlds
Bibliographic record
Abstract
The numerical simulation of biomechanical models enables the behavioral animation of realistic artificial animals in virtual worlds. Unfortunately, even on high-end graphics workstations, the biomechanical simulation approach is at present computationally too demanding for the animation of numerous animals at interactive frame rates. We tackle this problem by replacing biomechanical animal models with fast kinematic replicas that reproduce the locomotion abilities of the original models with reasonable fidelity. Our technique is based on capturing motion data by systematically simulating the biomechanical models. We refer to it as synthetic motion capture, because of the similarity to natural motion capture applied to real animals. We compile the captured motion data into kinematic action repertoires that are sufficiently rich to support elaborate behavioral animation. Synthetic motion capture in conjunction with level-of-detail geometric modeling and object culling during rendering has enabled us to transform a system designed for the realistic, off-line biomechanical/behavioral animation of artificial fishes into an interactive, stereoscopic, virtual undersea experience.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".