A fuzzy clustering algorithm for virtual character animation representation

The use of realistic humanoid animations generated through motion capture (MoCap) technology is widespread across various 3-D applications and industries. However, the existing compression techniques for such representation often do not consider the implicit coherence within the anatomical structure of a human skeletal model and lacks portability for transmission consideration. In this paper, a novel concept virtual character animation image (VCAI) is proposed. Built upon a fuzzy clustering algorithm, the data similarity within the anatomy structure of a virtual character (VC) model is jointly considered with the temporal coherence within the motion data to achieve efficient data compression. Since the VCA is mapped as an image, the use of image processing tool is possible for efficient compression and delivery of such content across dynamic network. A modified motion filter (MMF) is proposed to minimize the visual discontinuity in VCA's motion due to the quantization and transmission error. The MMF helps to remove high frequency noise components and smoothen the motion signal providing perceptually improved VCA with lessened distortion. Simulation results show that the proposed algorithm is competitive in compression efficiency and decoded VCA quality against the state-of-the-art VCA compression methods, making it suitable for providing quality VCA animation to low-powered mobile devices.