LAUSR

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 making the posture estimation cycle more autonomous. 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 a meta-model, which shows good generalization with respect to different individuals, backgrounds, and attire. This model allows robust interpretation of single video frames without temporal continuity and posture mimicking by an android robot.