Physically-Based Character Control in Low Dimensional Space

In this paper, we propose a new method to compose physically-based character controllers in low dimensional latent space. Source controllers are created by gradually updating the task parameter such as the external force applied to the body. During the optimization, instead of only saving the optimal controllers, we also keep a large number of non-optimal controllers. These controllers provide knowledge about the stable area in the controller space, and are then used as samples to construct a low dimensional manifold that represents stable controllers. During run-time, we interpolate controllers in the low dimensional space and create stable controllers to cope with the irregular external forces. Our method is best to be applied for real-time applications such as computer games.

[1]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[2]  Joshua B. Tenenbaum,et al.  Global Versus Local Methods in Nonlinear Dimensionality Reduction , 2002, NIPS.

[3]  Yoonsang Lee,et al.  Data-driven biped control , 2010, ACM Trans. Graph..

[4]  Hans-Peter Seidel,et al.  Real-time lens blur effects and focus control , 2010, SIGGRAPH 2010.

[5]  Tong-Yee Lee,et al.  Real-Time Physics-Based 3D Biped Character Animation Using an Inverted Pendulum Model , 2010, IEEE Transactions on Visualization and Computer Graphics.

[6]  Hyun Joon Shin,et al.  Motion synthesis and editing in low‐dimensional spaces , 2006, Comput. Animat. Virtual Worlds.

[7]  Aaron Hertzmann,et al.  Feature-based locomotion controllers , 2010, SIGGRAPH 2010.

[8]  David J. Fleet,et al.  Optimizing walking controllers for uncertain inputs and environments , 2010, ACM Trans. Graph..

[9]  Neil D. Lawrence,et al.  Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data , 2003, NIPS.

[10]  山田 祐,et al.  Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発 , 2007 .

[11]  Taesoo Kwon,et al.  Control systems for human running using an inverted pendulum model and a reference motion capture sequence , 2010, SCA '10.

[12]  KangKang Yin,et al.  SIMBICON: simple biped locomotion control , 2007, ACM Trans. Graph..

[13]  Jehee Lee,et al.  Motion synthesis and editing in low-dimensional spaces: Research Articles , 2006 .

[14]  Philippe Beaudoin,et al.  Continuation methods for adapting simulated skills , 2008, ACM Trans. Graph..

[15]  David J. Fleet,et al.  Optimizing walking controllers , 2009, ACM Trans. Graph..

[16]  Aaron Hertzmann,et al.  Style-based inverse kinematics , 2004, ACM Trans. Graph..

[17]  Shang-Hong Lai,et al.  Surface simplification by image retargeting , 2009, SIGGRAPH ASIA '09.

[18]  M. van de Panne,et al.  Generalized biped walking control , 2010, ACM Trans. Graph..

[19]  Marco da Silva,et al.  Interactive simulation of stylized human locomotion , 2008, ACM Trans. Graph..

[20]  Zoran Popovic,et al.  Optimal gait and form for animal locomotion , 2009, ACM Trans. Graph..

[21]  Michiel van de Panne,et al.  Guided Optimization for Balanced Locomotion , 1995 .

[22]  David A. Forsyth,et al.  Generalizing motion edits with Gaussian processes , 2009, ACM Trans. Graph..

[23]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[24]  Jerry Pratt,et al.  Velocity-Based Stability Margins for Fast Bipedal Walking , 2006 .

[25]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[26]  Martin de Lasa,et al.  Robust physics-based locomotion using low-dimensional planning , 2010, ACM Trans. Graph..