A compact model of human postures extracting common motion from individual samples

Model-based markerless human motion capture is often affected by instabilities of estimation mainly due to high degrees of freedom and inaccuracies in the body model. The authors propose a compact model of human postures which extracts common motion across different persons from individual samples. Our analysis on motion capture data shows that individualities appear as constant offsets that represent individual figures. The proposed model compactly describes the variations of postures in common motion by using a low-dimensional linear model. Experimental results show that our model gives moderate constraints to improve the accuracy of posture estimation from a single image of an unknown person whose body size is unknown.