DIGITAL MARIONETTE: augmenting kinematics with physics for multi-track desktop performance animation
Bibliographic record
Abstract
We have developed a novel desktop performance animation, DIGITAL MARIONETTE, which provides an interactive interface for animating graphical characters in real time using a minimal input configuration of just two motion trackers. By embedding the trackers in a physical tubing, we establish a tangible interface with the various coordinate frames inherent to the character. This provides the user with the kinesthetic, visual, tactile and inertial-based feedback for manipulating these frames. Building on this compatibility, we develop a multi-track motion recording framework and a set of mappings that enable the two 6-DOF trackers to control a 33-DOF character by progressively layering motions. Local physically-based motion actuators are introduced to augment an existing kinematic mapping, facilitating the animation task and enhancing the naturalness of the motion. They are designed so that the user is not burdened with the additional cognitive overhead of solving the balance control problem. Thus we integrate kinematics and dynamical principles for more effective real-time animation control. An extensive account of the experience of learning and using the D IGITAL MARIONETTE system is given. This focuses on the perspective of a user experienced both with this system, as well as with other complex interfaces; the learning process is contextualized, and some common principles are exposed. Three novice test users spent approximately two hours each working with our system, and were able to achieve the basics of a walk within that time. These results indicate that DIGITAL MARIONETTE's interface provides opportunity for in-depth exploration, learning and refinement in the creation of animation. Our desktop performance animation system enables the animator to fluidly and immediately play out his or her ideas of motion into animation, giving the animator a unique instrument for creating a range of truly expressive human character animation ranging from walking to dancing. DIGITAL MARIONETTE has been used in a live theatrical performance, and the motion created with it has been described by performers as “beautiful” and “graceful”. We have developed a powerful desktop tool which, via its unique tangible interface, local-physics based models, and layered mappings, allows very efficient creation of expressive animation.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".