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We introduce a new algorithm for real-time interactive motion control and demonstrate its application to motion captured data, prerecorded videos, and HCI. Firstly, a data set of frames are projected into a lower dimensional space. An appearance model is learnt using a multivariate probability distribution. A novel approach to determining transition points is presented based on k-medoids, whereby appropriate points of intersection in the motion trajectory are derived as cluster centers. These points are used to segment the data into smaller subsequences. A transition matrix combined with a kernel density estimation is used to determine suitable transitions between the subsequences to develop novel motion. To facilitate real-time interactive control, conditional probabilities are used to derive motion given user commands. The user commands can come from any modality including auditory, touch, and gesture. The system is also extended to HCI using audio signals of speech in a conversation to trigger nonverbal responses from a synthetic listener in real-time. We demonstrate the flexibility of the model by presenting results ranging from data sets composed of vectorized images, 2-D, and 3-D point representations. Results show real-time interaction and plausible motion generation between different types of movement.