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Schlagwörter:
Computer Science, Learning, cs.LG,Computer Science, Computer Vision and Pattern Recognition, cs.CV,Statistics, Machine Learning, stat.ML
Zusammenfassung:
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.
