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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
De Aguiar, E., Theobalt, C., Thrun, S., Seidel, H.-P.: Automatic conversion of mesh animations into skeleton-based animations. In: Computer Graphics Forum, vol. 27, no. 2, pp. 389–397. Wiley Online Library (2008)
Starck, J., Hilton, A.: Surface capture for performance-based animation. Comput. Graph. Appl. IEEE 27(3), 21–31 (2007)
Arcila, R., Cagniart, C., Hétroy, F., Boyer, E., Dupont, F.: Segmentation of temporal mesh sequences into rigidly moving components. Graph. Models 75(1), 10–22 (2013)
Tong, J., Zhou, J., Liu, L., Pan, Z., Yan, H.: Scanning 3d full human bodies using kinects. IEEE Trans. Vis. Comput. Graph. 18(4), 643–650 (2012)
Váša, L., Skala, V.: Cobra: compression of the basis for pca represented animations. In: Computer Graphics Forum, vol. 28, no. 6, pp. 1529–1540. Wiley Online Library (2009)
Breidt, M., Biilthoff, H.H., Curio, C.: Robust semantic analysis by synthesis of 3d facial motion. In: 2011 IEEE International Conference on Automatic Face and Gesture Recognition and Workshops (FG 2011), pp. 713–719. IEEE (2011)
Akhter, I., Simon, T., Khan, S., Matthews, I., Sheikh, Y.: Bilinear spatiotemporal basis models. ACM Trans. Graph. (TOG) 31(2), 17 (2012)
Romaszewski, M., Głomb, P.: Parameter estimation for hosvd-based approximation of temporally coherent mesh sequences. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 138–145 (2016)
Romaszewski, M., Gawron, P., Opozda, S.: Dimensionality reduction of dynamic animations using HO-SVD. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8467, pp. 757–768. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07173-2_65
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Jolliffe, I.: Principal Component Analysis. Wiley Online Library (2005)
Savran, A., et al.: Bosphorus database for 3D face analysis. In: Schouten, B., Juul, N.C., Drygajlo, A., Tistarelli, M. (eds.) BioID 2008. LNCS, vol. 5372, pp. 47–56. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89991-4_6
Zollhöfer, M., Martinek, M., Greiner, G., Stamminger, M., Süßmuth, J.: Automatic reconstruction of personalized avatars from 3D face scans. Comput. Animat. Virtual Worlds 22(2–3), 195–202 (2011). https://doi.org/10.1002/cav.405
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling 2001, pp. 145–152. IEEE (2001)
Shakhnarovich, G., Darrell, T., Indyk, P.: Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing). The MIT press (2006)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Romaszewski, M., Sochan, A., Skabek, K. (2018). Matrix and Tensor-Based Approximation of 3D Face Animations from Low-Cost Range Sensors. In: Czachórski, T., Gelenbe, E., Grochla, K., Lent, R. (eds) Computer and Information Sciences. ISCIS 2018. Communications in Computer and Information Science, vol 935. Springer, Cham. https://doi.org/10.1007/978-3-030-00840-6_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-00840-6_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00839-0
Online ISBN: 978-3-030-00840-6
eBook Packages: Computer ScienceComputer Science (R0)