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Matrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors

  • Michał Romaszewski
  • Arkadiusz Sochan
  • Krzysztof Skabek
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 935)

Abstract

Three-dimensional animation is often represented in the form of a sequence of 3D meshes, also called dynamic animation or Temporally Coherent Mesh Sequence (TCMS). Widespread availability of affordable range sensors makes capturing such data easy, however, its huge volume complicates both storage and further processing. One of the possible solutions is to approximate the data using matrix or tensor decomposition. However the quality the animation may have different impact on both approaches. In this work we use the Microsoft Kinect™ to crate sequences of human face models and compare the approximation error obtained from modelling animations using Principal component analysis (PCA) and Higher Order Singular Value Decomposition (HOSVD). We focus on distortion introduced by reconstruction of data from its truncated factorization. We show that while HOSVD may outperform PCA in terms of approximation error, it may be significantly affected by distortion in animation data.

Keywords

3D face models Approximation HOSVD PCA Kinect 

Notes

Acknowledgements

This work is partially based on results of the National Science for Research and Development projects: INNOTECH-K2/IN2/50/182645/NCBR/12 and National Science Centre, decision 2011/03/D/ST6/03753. Authors would like to thank Sebastian Opozda for his help with data visualization and development of experimental environment.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michał Romaszewski
    • 1
  • Arkadiusz Sochan
    • 1
  • Krzysztof Skabek
    • 1
  1. 1.Institute of Theoretical and Applied Informatics, Polish Academy of SciencesGliwicePoland

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