Abstract
We introduce a novel approach to support fast and efficient lossy compression of arbitrary animation sequences ideally suited for real-time scenarios, such as streaming and content creation applications, where input is not known a priori and is dynamically generated. The presented method exploits temporal coherence by altering the principal component analysis (PCA) procedure from a batch- to an adaptive-basis aiming to simultaneously support three important objectives: fast compression times, reduced memory requirements and high-quality reproduction results. A dynamic compression pipeline is presented that can efficiently approximate the k-largest PCA bases based on the previous iteration (frame block) at a significantly lower complexity than directly computing the singular value decomposition. To avoid errors when a fixed number of basis vectors are used for all frame blocks, a flexible solution that automatically identifies the optimal subspace size for each one is also offered. An extensive experimental study is finally offered, showing that the proposed methods are superior in terms of performance as compared to several direct PCA-based schemes while, at the same time, achieves plausible reconstruction output despite the constraints posed by arbitrarily complex animated scenarios.
References
Alexa, M., Müller, W.: Representing animations by principal components. Comput. Graph. Forum 19(3), 411–418 (2000)
Amjoun, R., Straßer, W.: Efficient compression of 3D dynamic mesh sequences. WSCG 15(1–3), 99–106 (2007)
Cheng, Z.Q., Liu, H.F., Jin, S.Y.: The progressive mesh compression based on meaningful segmentation. Vis. Comput. 23(9), 651–660 (2007)
Comon, P., Golub, G.H.: Tracking a few extreme singular values and vectors in signal processing. Proc. IEEE 78(8), 1327–1343 (1990)
Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins Studies in the Mathematical Sciences, Johns Hopkins University Press, Baltimore (1996)
Hua, Y.: Asymptotical orthonormalization of subspace matrices without square root. Signal Process. Mag. 21(4), 56–61 (2004)
Ibarria, L., Rossignac, J.: Dynapack: space-time compression of the 3D animations of triangle meshes with fixed connectivity. In: Proceedings of the 2003 SIGGRAPH/EG Symposium on Computer Animation (SCA’03), pp. 126–135. Aire-la-Ville, Switzerland (2003)
Jacobson, A., Deng, Z., Kavan, L., Lewis, J.: Skinning: Real-time shape deformation. In: ACM SIGGRAPH 2014 Courses (2014)
Karni, Z., Gotsman, C.: Compression of soft-body animation sequences. Comput. Graph. 28(1), 25–34 (2004)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput. 20(1), 359–392 (1998)
Kry, P.G., James, D.L., Pai, D.K.: Eigenskin: Real time large deformation character skinning in hardware. In: Proceedings of the 2002 SIGGRAPH/EG Symposium on Computer Animation (SCA’02), pp. 153–159 (2002)
Lalas, A., et al.: Numerical assessment of airflow and inhaled particles attributes in obstructed pulmonary system. In: 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 606–612 (2016)
Lalos, A., et al.: Compressed sensing for efficient encoding of dense 3D meshes using model-based Bayesian learning. IEEE Trans. Multimed. 19(1), 41–53 (2017)
Lee, D.Y., Sull, S., Kim, C.S.: Progressive 3D mesh compression using MOG-based Bayesian entropy coding and gradual prediction. Vis. Comput. 30(10), 1077–1091 (2014)
Luo, G., Cordier, F., Seo, H.: Compression of 3D mesh sequences by temporal segmentation. Comput. Anim. Virtual Worlds 24(3–4), 365–375 (2013)
Maglo, A., Lavoué, G., Dupont, F., Hudelot, C.: 3D mesh compression: survey, comparisons, and emerging trends. ACM Comput. Surv. 47(3), 44:1–44:41 (2015)
Mamou, K., Zaharia, T., Prêteux, F.: A skinning approach for dynamic 3D mesh compression. Comput. Anim. Virtual Worlds 17(3–4), 337–346 (2006)
Mamou, K., Zaharia, T., Prêteux, F.: TFAN: a low complexity 3D mesh compression algorithm. Comput. Animat. Virtual Worlds 20(2), 343–354 (2009)
Payan, F., Antonini, M.: Temporal wavelet-based compression for 3D animated models. Comput. Graph. 31(1), 77–88 (2007)
Rus, J., Váša, L.: Analysing the influence of vertex clustering on PCA-based dynamic mesh compression. In: Proceedings of the 6th International Conference on Articulated Motion and Deformable Objects (AMDO’10), pp. 55–66. Springer, Berlin (2010)
Saad, Y.: Analysis of subspace iteration for eigenvalue problems with evolving matrices. SIAM J. Matrix Anal. Appl. 37(1), 103–122 (2016)
Sattler, M., Sarlette, R., Klein, R.: Simple and efficient compression of animation sequences. In: Proceedings of the 2005 SIGGRAPH/EG Symposium on Computer Animation (SCA’05), pp. 209–217. ACM, NY, USA (2005)
Stefanoski, N., Ostermann, J.: SPC: fast and efficient scalable predictive coding of animated meshes. Comput. Graph. Forum 29(1), 101–116 (2010)
Strobach, P.: Fast recursive orthogonal iteration subspace tracking algorithms & applications. Signal Process. 59(1), 73–100 (1997)
Tian, J., et al.: Adaptive coding of generic 3D triangular meshes based on octree decomposition. Vis. Comput. 28(6), 819–827 (2012)
Váša, L., Marras, S., Hormann, K., Brunnett, G.: Compressing dynamic meshes with geometric laplacians. Comput. Graph. Forum 33(2), 145–154 (2014)
Váša, L., Skala, V.: CoDDyaC: Connectivity driven dynamic mesh compression. In: 3DTV Conference, pp. 1–4. IEEE (2007)
Váša, L., Skala, V.: COBRA: compression of the basis for PCA represented animations. Comput. Graph. Forum 28(6), 1529–1540 (2009)
Váša, L., Skala, V.: Geometry-driven local neighbourhood based predictors for dynamic mesh compression. Comput. Graph. Forum 29(6), 1921–1933 (2010)
Váša, L., Skala, V.: A perception correlated comparison method for dynamic meshes. IEEE Trans. Vis. Comput. Graph. 17(2), 220–230 (2011)
Vasilakis, A.A., Fudos, I.: Pose partitioning for multi-resolution segmentation of arbitrary mesh animations. Comput. Graph. Forum 33(2), 293–302 (2014)
Vasilakis, A.A., Fudos, I., Antonopoulos, G.: PPS: pose-to-pose skinning of animated meshes. In: Proceedings of the 33rd Computer Graphics International (CGI’16), pp. 53–56. ACM, New York, NY, USA (2016)
Xu, G., Kailath, T.: Fast estimation of principal eigenspace using Lanczos algorithm. SIAM J. Matrix Anal. Appl. 15(3), 974–994 (1994)
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This work has been supported by the H2020-PHC-2014 RIA project MyAirCoach (Grant No. 643607).
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Lalos, A.S., Vasilakis, A.A., Dimas, A. et al. Adaptive compression of animated meshes by exploiting orthogonal iterations. Vis Comput 33, 811–821 (2017). https://doi.org/10.1007/s00371-017-1395-4
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DOI: https://doi.org/10.1007/s00371-017-1395-4