Kinematic Transition Model for Training-free Online Human Motion Control

Interactive human motion synthesis aims to continually generate plausible motion while responding to changing user inputs and environmental constraints. Recent autoregressive motion models enable efficient online generation, but their real-time control typically relies on predefined conditioning signals, specialized control interfaces, or objective-specific training. In practical scenarios, however, control objectives may be newly introduced, changed, or combined during execution, requiring the motion generator to adapt without retraining while maintaining real-time performance. To address this challenge, we propose Kinematic Transition Model (KTM) for training-free online human motion control. Starting from the observed motion, KTM constructs an analytical kinematic continuation from its boundary dynamics and represents possible futures as acceleration corrections to this continuation. We then model future generation through a small number of latent stochastic transitions in this boundary-attached kinematic space, capturing the one-to-many evolution toward plausible future motions. Importantly, the same transition representation provides an efficient interface for training-free control. At each transition step, the predicted transition is kept fixed as a local motion prior while a future endpoint is optimized under the current inference-time objective. The optimized endpoint analytically defines the next controlled transition, allowing new or changing differentiable objectives to steer motion generation without control-specific training, additional motion-model computations, or backpropagation through the learned generator during optimization. Experiments demonstrate that KTM enables high-quality online motion synthesis with flexible control over unseen objectives while maintaining real-time inference.