Unsupervised hierarchical modeling of locomotion styles

ABSTRACT

This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the same motion performed by different subjects. Modeling motion styles requires identifying the common structure in the motions and detecting style-specific characteristics. We propose an algorithm that learns a hierarchical model of styles from unlabeled motion capture data by exploiting the cyclic property of human locomotion. We assume that sequences with the same style contain locomotion cycles generated by noisy, temporally warped versions of a single latent cycle. We model these style-specific latent cycles as random variables drawn from a common "parent" cycle distribution, representing the structure shared by all motions. Given these hierarchical priors, the algorithm learns, in a completely unsupervised fashion, temporally aligned latent cycle distributions, each modeling a specific locomotion style, and computes for each example the style label posterior distribution, the segmentation into cycles, and the temporal warping with respect to the latent cycles. We demonstrate the flexibility of the model on several application problems such as style clustering, animation, style blending, and filling in of missing data.

References

1. Brand, M., & Hertzmann, A. (2000). Style machines. Proc. of SIGGRAPH (pp. 183--192).
2. Chiappa, S., Kober, J., & Peters, J. (2009). Using bayesian dynamical systems for motion template libraries. In Adv. in Neural Inform. Proc. Systems 21, 297--304.
3. Elgammal, A. M., & Lee, C.-S. (2004). Separating style and content on a nonlinear manifold. Proc. of Comp. Vision Pattern Recogn. (pp. 478--485).
4. Grochow, K., Martin, S. L., Hertzmann, A., & Popovićć, Z. (2004). Style-based inverse kinematics. ACM Trans. on Graphics, 23, 522--531.
5. Jordan, M. I., Ghahramani, Z., Jaakkola, T., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37, 183--233.
6. Kovar, L., Gleicher, M., & Pighin, F. (2002). Motion graphs. ACM Trans. on Graphics, 21, 473--482.
7. Li, Y., Wang, T., & Shum, H.-Y. (2002). Motion texture: A two-level statistical model for character motion synthesis. ACM Trans. on Graphics, 21, 465--472.
8. Listgarten, J., Neal, R. M., Roweis, S. T., & Emili, A. (2005). Multiple alignment of continuous time series. In Adv. in Neural Inform. Proc. Systems 17, 817--824.
9. Listgarten, J., Neal, R. M., Roweis, S. T., Puckrin, R., & Cutler, S. (2007). Bayesian detection of infrequent differences in sets of time series with shared structure. In Adv. in Neural Inform. Proc. Systems 19, 905--912.
10. Liu, K., Hertzmann, A., & Popovic, Z. (2005). Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. on Graphics, 24, 1071--1081.
11. Ormoneit, D., Black, M., Hastie, T., & Kjellströöm, H. (2005). Representing cyclic human motion using functional analysis. Image and Vision Comp., 1264--1276.
12. Rabiner, L. R. (1989). A tutorial on HMMs and selected applications in speech recognition. Proc. IEEE, 77.
13. Rose, C., Cohen, M., & Bodenheimer, B. (1998). Verbs and adverbs: multidimensional motion interpolation. IEEE Computer Graphics and Application, 18, 32--40.
14. Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2007). Modeling human motion using binary latent variables. In Adv. in Neural Inform. Proc. Systems 19, 1345--1352.
15. Torresani, L., Hackney, P., & Bregler, C. (2007). Learning motion style synthesis from perceptual observations. In Adv. in Neural Inform. Proc. Systems 19, 1393--1400.
16. Urtasun, R., Fleet, D. J., Geiger, A., Popovic, J., Darrell, T., & Lawrence, N. D. (2008). Topologically-constrained latent variable models. Proc. Int. Conf. Machine Learning (pp. 1080--1087).
17. Wang, J. M., Fleet, D. J., & Hertzmann, A. (2007). Multi-factor gaussian process models for style-content separation. Proc. Int. Conf. Machine Learning (pp. 975--982).

Index Terms

• Computer systems organization

  • Embedded and cyber-physical systems

    • Robotics

      • Robotic components

• Computing methodologies

  • Artificial intelligence

    • Control methods

      • Motion path planning

  • Machine learning

  • Modeling and simulation

    • Model development and analysis

      • Model verification and validation

      • Modeling methodologies