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Human motion capture data compression by model-based indexing: a power aware approach.

Siddhartha Chattopadhyay1, Suchendra M Bhandarkar, Kang Li

  • 1Department of Computer Science, The University of Georgia, 415 Boyd Graduate Studies Research Center, Athens, GA 30602-7404, USA. siddh@cs.uga.edu

IEEE Transactions on Visualization and Computer Graphics
|November 10, 2006
PubMed
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This study introduces a new method for compressing human motion capture (MoCap) data. The novel algorithm reduces network bandwidth and power consumption for virtual human animation, offering visually acceptable quality.

Area of Science:

  • Computer Graphics
  • Virtual Reality
  • Data Compression

Background:

  • Human Motion Capture (MoCap) data is crucial for animating virtual characters in VR and games.
  • Standard MPEG-4 compression of MoCap data leads to high power consumption during decompression.

Purpose of the Study:

  • To propose a novel algorithm for efficient MoCap data compression.
  • To reduce network bandwidth and power consumption for MoCap decompression.
  • To maintain visually acceptable quality for virtual human animation.

Main Methods:

  • Developed a novel MoCap data compression algorithm utilizing smart indexing.
  • Exploited structural information from skeletal virtual human models.
  • Employed three quality control parameters (QCPs) for fine-tuning compression.

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Main Results:

  • The proposed algorithm significantly reduces network bandwidth requirements.
  • Demonstrated reduced power consumption during data decompression compared to MPEG-4.
  • Achieved visually acceptable quality for virtual human animation.

Conclusions:

  • The novel MoCap compression algorithm offers an efficient alternative to standard methods.
  • Smart indexing based on skeletal structure is key to reduced resource usage.
  • The method balances compression efficiency with animation quality.