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Posture normalisation of 3D body scans.

Femke Danckaers1, Toon Huysmans1,2, Ann Hallemans3,4

  • 1a imec - Vision Lab, Department of Physics , University of Antwerp , Belgium Universiteitsplein 1, B-2610 , Antwerp , Belgium.

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This study introduces a new framework for creating posture-invariant statistical shape models (SSMs). This method significantly improves the compactness of virtual mannequins used in product development.

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Area of Science:

  • Computer graphics
  • Human body modeling
  • Statistical analysis

Background:

  • Virtual mannequins are essential for near-body product design.
  • Statistical Shape Models (SSMs) describe body shape variability but are often affected by posture variations.
  • Posture variations reduce the compactness and efficiency of SSMs.

Purpose of the Study:

  • To propose a computationally efficient framework for building posture-invariant SSMs.
  • To capture and correct posture variations within SSMs.
  • To enhance the compactness of SSMs for improved usability.

Main Methods:

  • Developed a framework to capture and correct posture in shape instances.
  • Utilized statistical shape modeling techniques.
  • Evaluated the compactness of posture-normalized SSMs compared to traditional SSMs.

Main Results:

  • The proposed framework effectively removes posture variations from SSMs.
  • Posture-normalized SSMs are substantially more compact than non-normalized SSMs.
  • The method offers low computational complexity.

Conclusions:

  • The developed framework successfully creates posture-invariant SSMs.
  • Improved SSM compactness benefits product developers using virtual mannequins.
  • This approach enhances the efficiency of digital human modeling.