University of Houston

In general, the original size of motion capture files from MoCap system is prohibitively large due to the complexity of high dimensional motion information. In this work, we propose a novel scheme to compress original human motion capture data based on hierarchical structure construction and motion pattern indexing technology. Given a sequence of 3D motion capture data of human body, the 3D markers are first organized into a pre-defined hierarchy where each marker set corresponds to a meaningful part of the human body. Then, the motion sequence corresponding to each body part is coded and processed separately. Based on the observation that there is a high degree of spatial and temporal correlation among the 3D marker positions, we strive to identify motion patterns with indexes that form a database for each meaningful body part. Thereafter, a sequence of motion capture data can be efficiently represented as a series of motion pattern indices. As a result, higher compression ratio has been observed when compared with the prior art, especially for long sequences of motion capture data with repetitive motion styles. Another distinction of this work is that it provides means for flexible and intuitive global and local distortion controls to balance the trade-off between quality and performance.