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http://dx.doi.org/10.9717/kmms.2016.19.8.1587

Interactive Locomotion Controller using Inverted Pendulum Model with Low-Dimensional Data  

Han, KuHyun (Dept. of Computer Science and Engineering, Korea University)
Kim, YoungBeom (Realistic Media Platform Research Center, Korea Electronics Technology Institute)
Park, Byung-Ha (Realistic Media Platform Research Center, Korea Electronics Technology Institute)
Jung, Kwang-Mo (Realistic Media Platform Research Center, Korea Electronics Technology Institute)
Han, JungHyun (Dept. of Computer Science and Engineering, Korea University)
Publication Information
Abstract
This paper presents an interactive locomotion controller using motion capture data and inverted pendulum model. Most of the data-driven character controller using motion capture data have two kinds of limitation. First, it needs many example motion capture data to generate realistic motion. Second, it is difficult to make natural-looking motion when characters navigate dynamic terrain. In this paper, we present a technique that uses dimension reduction technique to motion capture data together with the Gaussian process dynamical model (GPDM), and interpolates the low-dimensional data to make final motion. With the low-dimensional data, we can make realistic walking motion with few example motion capture data. In addition, we apply the inverted pendulum model (IPM) to calculate the root trajectory considering the real-time user input upon the dynamic terrain. Our method can be used in game, virtual training, and many real-time applications.
Keywords
Animation; Interactive Locomotion Controller; Inverted Pendulum Model; Dimension Reduction; Real-time;
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1 L. Kovar, M. Gleicher, and F. Pighin, "Motion Graphs," ACM Transactions on Graphics, Vol. 21, No. 3, pp.473-482, 2002.   DOI
2 Y. Lee, K. Wampler, G. Bernstein, J. Popović and Z. Popović, "Motion Fields for Interactive Character Locomotion," ACM Transactions on Graphics, Vol. 29, No. 6, pp. 138, 2010.   DOI
3 S. Levine, J.M. Wang, A. Haraux, Z. Popović, and V. Koltun, "Continuous Character Control with Low-Dimensional Embeddings," ACM Transactions on Graphics, Vol. 31, No. 4, pp. 28, 2012.   DOI
4 Y.Y. Tsai, W.C. Lin, K.B. Cheng, J. Lee, and T.Y. Lee, "Real-Time Physics-Based 3D Biped Character Animation Using an Inverted Pendulum Model," IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 2, pp. 325-337, 2010.   DOI
5 I. Mordatch, M. De Lasa, and A. Hertzmann, "Robust Physics-Based Locomotion Using Low-Dimensional Planning," ACM Transactions on Graphics, Vol. 29, No. 4, pp. 71, 2010.   DOI
6 T. Kwon, and J. Hodgins, "Control Systems for Human Running Using an Inverted Pendulum Model and a Reference Motion Capture Sequence," Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, pp. 129-138, 2010.
7 B. Kenwright, R. Davison, and G. Morgan, "Dynamic Balancing and Walking for Real-Time 3D Characters," Proceeding of Motion in Games, Springer Berlin Heidelberg, pp. 63-73, 2011.
8 S. Kajita, F. Kanehiro, K. Kaneko, K. Yokoi, and H. Hirukawa, "The 3D Linear Inverted Pendulum Mode: A Simple Modeling for a Biped Walking Pattern Generation," Proceeding of Intelligent Robots and Systems, International Conference on IEEE/RSJ , pp. 239-246, 2001.
9 N.D. Lawrence, "Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data," Journal fo Advances in Neural Information Processing Systems, Vol. 16, No. 3, pp. 329-336, 2004.
10 J.M. Wang, D.J. Fleet, and A. Hertzmann. "Gaussian Process Dynamical Models for Human Motion," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 2, pp. 283-298, 2008.   DOI
11 K. Grochow, S.L. Martin, A. Hertzmann, and Z. Popović, "Style-Based Inverse Kinematics," ACM Transactions on Graphics, Vol. 23, No. 3, pp. 522-531, 2004.   DOI
12 K. Yu, "Automatic Generation of Character-Specific Roadmaps for Path Planning in Computer Games," Journal of Korea Multimedia Society, Vol. 11, No. 5, pp. 692-702, 2008.
13 N.D. Lawrence and A.J. Moore, "Hierarchical Gaussian Process Latent Variable Models," Proceedings of the 24th International Conference on Machine Learning, pp. 481-488, 2007.