A spatial hierarchical compression method for 3D streaming animation
VRML '00 Proceedings of the fifth symposium on Virtual reality modeling language (Web3D-VRML)
Advanced real-time collaboration over the internet
VRST '00 Proceedings of the ACM symposium on Virtual reality software and technology
A Dead-Reckoning Algorithm for Virtual Human Figures
VRAIS '97 Proceedings of the 1997 Virtual Reality Annual International Symposium (VRAIS '97)
User Interaction in Mixed Reality Interactive Storytelling
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Proceedings of the ACM symposium on Virtual reality software and technology
Compression of motion capture databases
ACM SIGGRAPH 2006 Papers
Simulating Virtual Humans in Networked Virtual Environments
Presence: Teleoperators and Virtual Environments
Learn to compress and restore sequential data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Quaternion space sparse decomposition for motion compression and retrieval
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Quaternion space sparse decomposition for motion compression and retrieval
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
The alpha parallelogram predictor: A lossless compression method for motion capture data
Information Sciences: an International Journal
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Human Motion Capture (MoCap) data can be used for animation of virtual human-like characters in distributed virtual reality applications and networked games. MoCap data compressed using the standard MPEG-4 encoding pipeline comprising of predictive encoding (and/or DCT decorrelation), quantization, and arithmetic/Huffman encoding, entails significant power consumption for the purpose of decompression. In this paper, we propose a novel algorithm for compression of MoCap data, which is based on smart indexing of the MoCap data by exploiting structural information derived from the skeletal virtual human model. The indexing algorithm can be fine-controlled using three predefined quality control parameters (QCPs). We demonstrate how an efficient combination of the three QCPs results in a lower network bandwidth requirement and reduced power consumption for data decompression at the client end when compared to standard MPEG-4 compression. Since the proposed algorithm exploits structural information derived from the skeletal virtual human model, it is observed to result in virtual human animation of visually acceptable quality upon decompression.