Intelligent toolkit for procedural animation of human behaviors
Bibliographic record
Abstract
The use of optical motion capture systems to produce high quality humanoid motion sequences has recently become increasingly popular in many areas such as high-end gaming, cinema movie and animation production, and for educational purposes. Motion capture studios are expensive to employ or are unavailable to many people and locales. For this reason, the reuse of existing motion capture data and the resulting high-quality animation is an important area of research. Strict motion capture data restricts users to pre-recorded movement that does not allow the addition of dynamic behaviors required in advanced games and interactive environment. Our procedural animation method offers animators high quality animations produced from an optical motion capture session, without incurring the cost of running their own sessions. This method utilizes a database of common animations sequences, derived from several motion capture sessions, which animators can manipulate and apply to their own existing characters through the use of our procedural animation toolkit. The basic animation types include walk, run, and jump sequences that can be applied with personality variations that correspond to a character's gender, age, and energy levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".