Abstract
This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skeletal size from a small set of 2D image features tracked from uncalibrated monocular video sequences. The key idea of our approach is to construct a generative human motion model from a large set of preprocessed human motion examples to constrain the solution space of monocular human motion tracking. In addition, we learn a generative skeleton model from prerecorded human skeleton data to reduce ambiguity of the human skeleton reconstruction. We formulate the reconstruction process in a nonlinear optimization framework by continuously deforming the generative models to best match a small set of 2D image features tracked from a monocular video sequence. We evaluate the performance of our system by testing the algorithm on a variety of uncalibrated monocular video sequences.
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
Preview
Unable to display preview. Download preview PDF.
References
Howe, N., Leventon, M., Freeman, W.: Bayesian reconstruction of 3d human motion from single-camera video. In: Advances in Neural Information Processing Systems, vol. 12, pp. 820–826 (1999)
Brand, M.: Shadow puppetry. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1237–1244 (1999)
Pavlović, V., Rehg, J.M., MacCormick, J.: Learning switching linear models of human motion. In: Advances in Neural Information Processing Systems, vol. 13, pp. 981–987 (2000)
Rosales, R., Sclaroff, S.: Infering body pose without tracking body parts. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 506–511 (2000)
Agarwal, A., Triggs, B.: 3d human pose from silhouettes by relevance vector regression. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 882–888 (2004)
Elgammal, A., Lee, C.: Inferring 3d body pose from silhouettes using activity manifold learning. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 681–688 (2004)
Ormoneit, D., Sidenbladh, H., Black, M., Hastie, T.: Learning and tracking cyclic human motion. In: Advances in Neural Information Processing Systems, vol. 13, pp. 894–900 (2001)
Troje, N.: Decomposing biological motion: A framework for analysis and synthesis of human gait patterns. Journal of Vision 2(5), 371–387 (2002)
Urtasun, R., Fleet, D.J., Fua, P.: Temporal motion models for monocular and multiview 3d human body tracking. Computer Vision and Image Understanding (CVIU) 104(2), 157–177 (2006)
Fua, P.: Regularized bundle-adjustment to model heads from image sequences without calibration data. International Journal of Computer Vision 38(2), 153–171 (2000)
DiFranco, D., Cham, T.J., Rehg, J.M.: Reconstruction of 3d figure motion from 2d correspondences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 307–314 (2001)
Kovar, L., Gleicher, M.: Flexible automatic motion blending with registration curves. In: ACM SIGGRAPH/EUROGRAPH Symposium on Computer Animation, pp. 214–224 (2003)
Wei, X., Chai, J.: Interactive tracking of 2d generic objects with spacetime optimizatione. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 657–670. Springer, Heidelberg (2008)
Lourakis, M.: Levmar: Levenberg-marquardt nonlinear least squares algorithms in C/C++ (2009), http://www.ics.forth.gr/~lourakis/levmar/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, YL., Chai, J. (2010). 3D Reconstruction of Human Motion and Skeleton from Uncalibrated Monocular Video. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-12307-8_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
eBook Packages: Computer ScienceComputer Science (R0)