A System View of the Recognition and Interpretation of Observed Human Shape, Pose and Action

TL;DR

This paper presents a computational model inspired by brain mechanisms that unifies visual and motor processes for recognizing and interpreting human shape, pose, and action from monocular images, combining neurobiological insights with machine vision.

Contribution

It introduces a neurobiologically inspired system that solves inverse problems in human figure recognition and pose estimation using the Map-seeking Circuit algorithm, integrating visual and motor functions.

Findings

The model can reconstruct 3D human pose from monocular images.

It unifies visual transformation, inverse kinematics, and morphology adaptation.

The system demonstrates unique capabilities in human figure recognition and pose estimation.

Abstract

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.