Puppet Master: designing reactive character behavior by demonstration
Bibliographic record
Abstract
Puppet Master is a system that enables designers to rapidly create interactive and autonomous animated charac-ter behaviors that react to a main character controlled by an end-user. The behavior is designed by demonstration, allowing non-technical artists to intuitively design the style, personality, and emotion of the character, traits which are very difficult to design using conventional programming. During training, designers demonstrate paired be-havior between the main and reacting characters. During run time, the end user controls the main character and the system synthesizes the motion of the reacting character using the given training data. The algorithm is an extension of Image Analogies [HJO∗01], modified to synthesize dynamic character behavior instead of an image. We introduce non-trivial extensions to the algorithm such as our selection of features, dynamic balancing between similarity metrics, and separate treatment of path trajectory and high-frequency motion texture. We implemented a prototype system using physical pucks tracked by a motion-capture system and conducted a user study demon-strating that novice users can easily and successfully design character personality and emotion using our system and that the resulting behaviors are meaningful and engaging.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".