# Unsupervised Construction of Human Body Models Using Principles of Organic Computing

## TL;DR

This paper introduces an unsupervised approach to building generalizable human body models from video data by applying Organic Computing principles, enabling autonomous behavior understanding and robust interpretation without human intervention.

## Contribution

It integrates Organic Computing principles into posture estimation, creating models that generalize across individuals and environments without supervision.

## Key findings

- Models generalize well to different individuals and backgrounds.
- System can interpret single frames without temporal data.
- Enables posture mimicking on an android robot.

## Abstract

Unsupervised learning of a generalizable model of the visual appearance of humans from video data is of major importance for computing systems interacting naturally with their users and others. We propose a step towards automatic behavior understanding by integrating principles of Organic Computing into the posture estimation cycle, thereby relegating the need for human intervention while simultaneously raising the level of system autonomy. The system extracts coherent motion from moving upper bodies and autonomously decides about limbs and their possible spatial relationships. The models from many videos are integrated into meta-models, which show good generalization to different individuals, backgrounds, and attire. These models allow robust interpretation of single video frames without temporal continuity and posture mimicking by an android robot.