01416nas a2200145   4500000000100000000000100001008004100002260004400043100002400087700002100111700002100132245009200153520100300245020002201248       2018                            d  bSpringer International PublishingaCham1 aMichał Romaszewski1 aArkadiusz Sochan1 aKrzysztof Skabek00aMatrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors3 a<p>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™&nbsp;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.</p>
  a978-3-030-00840-6