Inverse Kinematics Using Sequential Monte Carlo Methods

In this paper we propose an original approach to solve the Inverse Kinematics problem. Our framework is based on Sequential Monte Carlo Methods and has the advantage to avoid the classical pitfalls of numerical inversion methods since only direct calculations are required. The resulting algorithm accepts arbitrary constraints and exhibits linear complexity with respect to the number of degrees of freedom. Hence, the proposed system is far more efficient for articulated figures with a high number of degrees of freedom.

[1]  Ian D. Reid,et al.  Articulated Body Motion Capture by Stochastic Search , 2005, International Journal of Computer Vision.

[2]  Daniel Thalmann,et al.  Interactive low-dimensional human motion synthesis by combining motion models and PIK , 2007 .

[3]  Kwang-Jin Choi,et al.  Online motion retargetting , 2000, Comput. Animat. Virtual Worlds.

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

[5]  Michael Patrick Johnson,et al.  Exploiting quaternions to support expressive interactive character motion , 2003 .

[6]  Neil J. Gordon,et al.  Editors: Sequential Monte Carlo Methods in Practice , 2001 .

[7]  Jessica K. Hodgins,et al.  Constraint-based motion optimization using a statistical dynamic model , 2007, SIGGRAPH 2007.

[8]  Cristian Sminchisescu,et al.  Fast mixing hyperdynamic sampling , 2006, Image Vis. Comput..

[9]  Hyeong-Seok Ko,et al.  A physically-based motion retargeting filter , 2005, TOGS.

[10]  Ronan Boulic,et al.  An inverse kinematics architecture enforcing an arbitrary number of strict priority levels , 2004, The Visual Computer.

[11]  Daniel Thalmann,et al.  Using an Intermediate Skeleton and Inverse Kinematics for Motion Retargeting , 2000, Comput. Graph. Forum.

[12]  Katsu Yamane,et al.  Natural Motion Animation through Constraining and Deconstraining at Will , 2003, IEEE Trans. Vis. Comput. Graph..

[13]  Ronan Boulic,et al.  Interactive motion deformation with prioritized constraints , 2004, SCA '04.

[14]  Oussama Khatib,et al.  Synthesis of Whole-Body Behaviors through Hierarchical Control of Behavioral Primitives , 2005, Int. J. Humanoid Robotics.

[15]  Tamim Asfour,et al.  An integrated approach to inverse kinematics and path planning for redundant manipulators , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[16]  Timothy J. Robinson,et al.  Sequential Monte Carlo Methods in Practice , 2003 .