Top

Published in:

2020 | OriginalPaper | Chapter

Motion Capture Analysis and Reconstruction Using Spatial Keyframes

Authors : Bernardo F. Costa, Claudio Esperança

Published in: Computer Vision, Imaging and Computer Graphics Theory and Applications

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Motion capturing is the preferred technique to create realistic animations for skeleton-based models. Capture sessions, however, are costly and the resulting motions are hard to analyze for posterior modification and reuse. In this paper we propose several tools to analyze and reconstruct motions using the concept of spatial keyframes, idealized by Igarashi et al. [19]. Captured motions are represented by curves on the plane obtained by multidimensional projection, allowing the animator to associate regions on that plane with regions in pose space so that representative poses can be located and harvested. The problem of reconstruction from representative poses is also investigated by conducting experiments that measure the error behavior with respect to different multidimensional projection and interpolation algorithms. In particular, we introduce a novel multidimensional projection optimization that minimizes reconstruction errors. These ideas are showcased in an interactive application that can be publicly accessed online.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Synthesising Light Field Volume Visualisations Using Image Warping in Real-Time

next chapter Involving Hearing, Haptics and Kinesthetics into Non-visual Interaction Concepts for an Augmented Remote Tower Environment

The learning rate appears in [26] as \(\eta \) in its simple version of the t-SNE algorithm. We used the value of 10 which produces less projection artifacts.

CMU graphics lab motion capture database. http://mocap.cs.cmu.edu/. Accessed 30 June 2015

The daz-friendly BVH release of CMU’s motion capture database - cgspeed. https://sites.google.com/a/cgspeed.com/cgspeed/motion-capture/daz-friendly-release. Accessed 19 May 2017

Alemi, O., Pasquier, P., Shaw, C.: Mova: Interactive movement analytics platform. In: Proceedings of the 2014 International Workshop on Movement and Computing, MOCO 2014, pp. 37:37–37:42. ACM, New York (2014). https://doi.org/10.1145/2617995.2618002. http://doi.acm.org/10.1145/2617995.2618002

Amorim, E., et al.: Facing the high-dimensions: Inverse projection with radial basis functions. Comput. Graph. 48, 35–47 (2015). https://doi.org/10.1016/j.cag.2015.02.009. http://www.sciencedirect.com/science/article/pii/S0097849315000230CrossRef

Amorim, E., Vital Brazil, E., Nonato, L.G., Samavati, F., Sousa, M.C.: Multidimensional projection with radial basis function and control points selection. In: Pacific Visualization Symposium (PacificVis), 2014 IEEE, Pacific Vis 2014, pp. 209–216 (2014). https://doi.org/10.1109/PacificVis.2014.59

Arikan, O.: Compression of motion capture databases. ACM Trans. Graph. 25(3), 890–897 (2006). https://doi.org/10.1145/1141911.1141971. http://doi.acm.org/10.1145/1141911CrossRefMATH

Assa, J., Caspi, Y., Cohen-Or, D.: Action synopsis: pose selection and illustration. ACM Trans. Graph. 24(3), 667–676 (2005). https://doi.org/10.1145/1073204.1073246. http://doi.acm.org/10.1145/1073204.1073246CrossRef

Bernard, J., Wilhelm, N., Krüger, B., May, T., Schreck, T., Kohlhammer, J.: MotionExplorer: Exploratory search in human motion capture data based on hierarchical aggregation. IEEE Trans. Vis. Comput. Graph. 19(12), 2257–2266 (2013). https://doi.org/10.1109/TVCG.2013.178CrossRef

Bulut, E., Capin, T.: Key frame extraction from motion capture data by curve saliency. In: Computer Animation and Social Agents, p. 119 (2007)

10.

Burges, C., et al.: Learning to rank using gradient descent. In: Proceedings of the 22Nd International Conference on Machine Learning, ICML 2005, pp. 89–96. ACM, New York (2005). https://doi.org/10.1145/1102351.1102363. http://doi.acm.org/10.1145/1102351.1102363

11.

Chen, S., Chng, E.S., Alkadhimi, K.: Regularized orthogonal least squares algorithm for constructing radial basis function networks. Int. J. Control 64(5), 829–837 (1996). https://doi.org/10.1080/00207179608921659MathSciNetCrossRefMATH

12.

Costa., B.F., Esperança., C.: Enhancing spatial keyframe animations with motion capture. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, pp. 31–40. INSTICC, SciTePress (2019). https://doi.org/10.5220/0007296800310040

13.

Cox, M.A.A., Cox, T.F.: Multidimensional Scaling, pp. 315–347. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-33037-0_14CrossRefMATH

14.

Dinh, H.Q., Turk, G., Slabaugh, G.: Reconstructing surfaces by volumetric regularization using radial basis functions. IEEE Trans. Pattern Anal. Mach. Intell. 24(10), 1358–1371 (2002). https://doi.org/10.1109/TPAMI.2002.1039207CrossRef

15.

Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartogr. Int. J. Geogr. Inf. Geovis. 10(2), 112–122 (1973). https://doi.org/10.3138/FM57-6770-U75U-7727CrossRef

16.

Halit, C., Capin, T.: Multiscale motion saliency for keyframe extraction frommotion capture sequences. Comput. Animat. Virtual Worlds 22(1), 3–14 (2011). https://doi.org/10.1002/cav.380CrossRef

17.

Hu, Y., Wu, S., Xia, S., Fu, J., Chen, W.: Motion track: Visualizing variations of human motion data. In: 2010 IEEE Pacific Visualization Symposium (PacificVis), pp. 153–160, March 2010. https://doi.org/10.1109/PACIFICVIS.2010.5429596

18.

Huang, K.S., Chang, C.F., Hsu, Y.Y., Yang, S.N.: Key probe: a technique for animation keyframe extraction. Vis. Comput. 21(8), 532–541 (2005). https://doi.org/10.1007/s00371-005-0316-0CrossRef

19.

Igarashi, T., Moscovich, T., Hughes, J.F.: Spatial keyframing for performance-driven animation. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2005, pp. 107–115. ACM, New York (2005). https://doi.org/10.1145/1073368.1073383

20.

Jenkins, O.C., Matarić, M.J.: A spatio-temporal extension to isomap nonlinear dimension reduction. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 56. ACM, New York (2004). https://doi.org/10.1145/1015330.1015357. http://doi.acm.org/10.1145/1015330.1015357

21.

Jin, C., Fevens, T., Mudur, S.: Optimized keyframe extraction for 3D character animations. Comput. Anim. Virtual Worlds 23(6), 559–568 (2012). https://doi.org/10.1002/cav.1471CrossRef

22.

Joia, P., Paulovich, F., Coimbra, D., Cuminato, J., Nonato, L.: Local affine multidimensional projection. IEEE Trans. Vis. Comput. Graph. 17(12), 2563–2571 (2011). https://doi.org/10.1109/TVCG.2011.220CrossRef

23.

Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)CrossRef

24.

Li, W., Bartram, L., Pasquier, P.: Techniques and approaches in static visualization of motion capture data. In: Proceedings of the 3rd International Symposium on Movement and Computing, MOCO 2016, pp. 14:1–14:8. ACM, New York (2016). https://doi.org/10.1145/2948910.2948935. http://doi.acm.org/10.1145/2948910.2948935

25.

Lim, I.S., Thalmann, D.: Key-posture extraction out of human motion data by curve simplification (2001). swiss Federal Inst. Technol. (EPFL), CH-1015 Laussane, Switzerland, http://infoscience.epfl.ch/record/98933

26.

van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008). http://www.jmlr.org/papers/v9/vandermaaten08a.htmlMATH

27.

Ramer, U.: An iterative procedure for the polygonal approximation of plane curves. Comput. Graph. Image Process. 1(3), 244–256 (1972). https://doi.org/10.1016/S0146-664X(72)80017-0. http://www.sciencedirect.com/science/article/pii/S0146664X72800170CrossRef

28.

Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000). https://doi.org/10.1126/science.290.5500.2323. http://science.sciencemag.org/content/290/5500/2323CrossRef

29.

Safonova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. Graph. 23(3), 514–521 (2004). https://doi.org/10.1145/1015706.1015754. http://doi.acm.org/10.1145/1015706.1015754CrossRef

30.

Sakamoto, Y., Kuriyama, S., Kaneko, T.: Motion map: image-based retrieval and segmentation of motion data. In: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2004, pp. 259–266. Eurographics Association, Goslar Germany, Germany (2004). https://doi.org/10.1145/1028523.1028557

31.

dos Santos Amorim, E.P., Brazil, E.V., II, Daniels, J., Joia, P., Nonato, L.G., Sousa, M.C.: iLamp: Exploring high-dimensional spacing through backward multidimensional projection. In: IEEE VAST, pp. 53–62. IEEE Computer Society (2012). http://dblp.uni-trier.de/db/conf/ieeevast/ieeevast2012.html#AmorimBDJNS12

32.

Shoemake, K.: Animating rotation with quaternion curves. In: ACM SIGGRAPH Computer Graphics, vol. 19, pp. 245–254. ACM (1985)

33.

Tejada, E., Minghim, R., Nonato, L.G.: On improved projection techniques to support visual exploration of multidimensional data sets. Inf. Vis. 2(4), 218–231 (2003). https://doi.org/10.1057/palgrave.ivs.9500054. https://doi.org/10.1057/palgrave.ivs.9500054CrossRef

34.

Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000). https://doi.org/10.1126/science.290.5500.2319. http://science.sciencemag.org/content/290/5500/2319CrossRef

35.

Togawa, H., Okuda, M.: Position-based keyframe selection for human motion animation. In: 11th International Conference on Parallel and Distributed Systems, 2005, Proceedings, vol. 2, pp. 182–185, July 2005. https://doi.org/10.1109/ICPADS.2005.239

36.

Wattenberg, M., Viégas, F., Johnson, I.: How to use t-sne effectively. Distill (2016). https://doi.org/10.23915/distill.00002. http://distill.pub/2016/misread-tsne

37.

Xiao, J., Zhuang, Y., Yang, T., Wu, F.: An efficient keyframe extraction from motion capture data. In: Nishita, T., Peng, Q., Seidel, H.-P. (eds.) CGI 2006. LNCS, vol. 4035, pp. 494–501. Springer, Heidelberg (2006). https://doi.org/10.1007/11784203_44CrossRef

38.

Zhang, Y., Cao, J.: A novel dimension reduction method for motion capture data and application in motion segmentation. In: 11th International Conference on Parallel and Distributed Systems, 2005, Proceedings, vol. 12, pp. 6751–6760 (2015). https://doi.org/10.12733/jics20150024CrossRef

Title: Motion Capture Analysis and Reconstruction Using Spatial Keyframes
Authors: Bernardo F. Costa
Claudio Esperança
Publisher: Springer International Publishing
Book: Computer Vision, Imaging and Computer Graphics Theory and Applications
Print ISBN: 978-3-030-41589-1

Electronic ISBN: 978-3-030-41590-7

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-41590-7_3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner