A clustering compression method for 3D Human motion capture data

Human motion capturing has become an important tool in fields such as sports sciences, biometrics, and particularly in computer animation, where large collections of motion material are accumulated in the production process. Efficient storage, retrieval and transmission methods are needed to fully exploit motion databases for reuse and for the synthesis of motions. In this paper, a compression method for 3D Human motion data is proposed. We represent and compress the motion data using the clustering method and primary component analysis. The compressed data is adapted to network transmission with shorter time in order to maximize the use of network bandwidth and computational performance of local machines. At the client, we decompress the motion chips and rebuild corresponding human motion. Experimental evaluation of the method showed that the proposed method has high compression rate and is effective.