  There is physiological evidence that our ability to interpret human pose and
action from 2D visual imagery (binocular or monocular) engages the circuitry of
the motor cortices as well as the visual areas of the brain. This implies that
the capability of the motor cortices to solve inverse kinematics is flexible
enough to apply to both motion planning as well as serving as a generative
model for the visual processing of human figures, despite the differing
functional requirements of the two tasks. This paper provides a computational
model of the cooperation between visual and motor areas: in other words, a
system view of an important class of brain computations. The model unifies the
solution of the separate inverse problems involved in the task, visual
transformation discovery, inverse kinematics, and adaptation to morphology
variations, using several instances of the Map-seeking Circuit algorithm. While
the paper is weighted toward the exposition of a neurobiological hypothesis,
from mathematical formalization of the problem to neuronal circuitry, the
algorithmic expression of the solution is also a functional machine vision
system for human figure recognition, and 3D pose and body morphology
reconstruction from monocular, perspective-less input imagery. With an inverse
kinematic generative model capable of imposing a variety of endogenous and
exogenous constraints the machine vision implementation acquires
characteristics currently unique among such systems.
