TY - JOUR
T1 - A fuzzy clustering algorithm for virtual character animation representation
AU - Chew, Boon Seng
AU - Chau, Lap Pui
AU - Yap, Kim Hui
N1 - Funding Information:
Manuscript received January 14, 2010; revised May 17, 2010; accepted September 21, 2010. Date of publication September 30, 2010; date of current version January 19, 2011. The data used in this project was obtained from mocap.cs.cmu.edu. The database was created with funding from NSF EIA-0196217. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Nadia Magnenat-Thalmann.
PY - 2011/2
Y1 - 2011/2
N2 - 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.
AB - 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.
KW - Compression
KW - fuzzy c-mean clustering and realism
KW - virtual character animation
UR - http://www.scopus.com/inward/record.url?scp=78951489089&partnerID=8YFLogxK
U2 - 10.1109/TMM.2010.2082512
DO - 10.1109/TMM.2010.2082512
M3 - Journal article
AN - SCOPUS:78951489089
SN - 1520-9210
VL - 13
SP - 40
EP - 49
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 1
M1 - 5593220
ER -