Convex Optimisation for Inverse Kinematics
This addresses the problem of local optima in inverse kinematics for applications like tracking and reconstruction in vision and graphics, offering a more reliable solution.
The paper tackles the inverse kinematics problem for articulated objects by proposing a convex optimization approach based on semidefinite programming, which globally solves a relaxation of the problem and significantly outperforms local optimization methods in experiments with real-world skeletons.
We consider the problem of inverse kinematics (IK), where one wants to find the parameters of a given kinematic skeleton that best explain a set of observed 3D joint locations. The kinematic skeleton has a tree structure, where each node is a joint that has an associated geometric transformation that is propagated to all its child nodes. The IK problem has various applications in vision and graphics, for example for tracking or reconstructing articulated objects, such as human hands or bodies. Most commonly, the IK problem is tackled using local optimisation methods. A major downside of these approaches is that, due to the non-convex nature of the problem, such methods are prone to converge to unwanted local optima and therefore require a good initialisation. In this paper we propose a convex optimisation approach for the IK problem based on semidefinite programming, which admits a polynomial-time algorithm that globally solves (a relaxation of) the IK problem. Experimentally, we demonstrate that the proposed method significantly outperforms local optimisation methods using different real-world skeletons.