Control Operators for Interactive Character Animation
Bibliographic record
Abstract
Neural-network-based character controllers are increasingly common and capable. However, the integration of desired control inputs such as joystick movement, motion paths, and objects in the environment, remains challenging. This is because these inputs often require custom feature engineering, specific neural network architectures, and training procedures. This renders these methods largely inaccessible to non-technical designers. To address this challenge, we introduce Control Operators , a powerful and flexible framework for specifying the control mechanisms of interactive character controllers. By breaking down the control problem into a set of simple operators, each with a semantic meaning for designers, and a corresponding neural network structure, we allow non-technical users to design control mechanisms in a way that is intuitive and can be composed together to train models that have multiple skills and control modes. We demonstrate their potential with two current state-of-the-art interactive character controllers - a Flow-Matching-based auto-regressive model, and a variation of Learned Motion Matching. We validate the approach via a user study wherein industry practitioners with varying degrees of ML and technical expertise explore the use of our system.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".