Abstract
We propose a technique called short-term principal component analysis (ST-PCA) to analyze motion capture (MoCap) data of realistic movements in a high dimensional time series. Our ST-PCA method is successfully applied to motion beat induction, which is an important aspect in human perception. ST-PCA performs PCA in a sliding window to locally extract the major variance of the movement into the coordinates of the first principal component, thus accurately determining the desired motion beats. Our approach differs from conventional methods in that we estimate the motion beats by analyzing the motion signals as a whole rather than individually in each channel. Moreover, our algorithm is carefully designed in terms of the three characteristics of MoCap data: hierarchical structure, spatial correlation, and temporal coherence. Experimental results demonstrate that the proposed method outputs much more accurate motion beats in a wide range of motion categories, including complicated dances, than current state-of-the-art alternatives.
