In this work, we record a dancer's rhythmic movement with background music. The captured motion sequences are then segmented into dozens of motion clips, to construct a motion database consisting of sets of labeled motion clips. Many of these motion clips contain short and rapid transition from one main dancing motion to another, which causes unnatural, awkward movements when they are connected in different orders than the original sequence. In this paper, we describe our approach for automatically detecting the transition parts in the segmented motion clips. For each motion clip, we model the motion data using the Gaussian mixture model (GMM) and use the resulting distribution cluster map to improve the efficiency and convergence of the clustering, principal component analysis (PCA) has been applied to the motion data prior to performing GMM. Experiments and comparative analysis show that this PCA-based GMM method effectively performs transition detection on the segmented motion clips.

We present our framework that efficiently synchronizes dancing motion of a virtual character according to music input. Two modules have been developed to achieve this. A music server analyzes audio input on the fly and extracts information on music such as tempo, time signature, and beat time. A motion client chooses and displays motion clips according to the music information, by time-warping and synchronizing the start time of motion clips with the music beat time. As a preprocessing step, we construct a database of motion clips using captured dancing motion of a dancer. A PCA-based method is presented to easily identify transiting parts from each motion clip.