Experimenting with nonintrusive motion capture in a virtual environment

A growing number of promising applications requires recognizing human posture and motion. Conventional techniques require us to attach foreign objects to the body, which in some applications is disturbing or even impossible. New, nonintrusive motion capture approaches are called for. The well-known shape-from-silhouette technique for understanding 3D shapes could also be effective for human bodies. We present a novel technique for model-based motion capture that uses silhouettes extracted from multiple views. A 3D reconstruction of the performer can be computed from a silhouette with a technique known as volume intersection. We can recover the posture by fitting a model of the human body to the reconstructed volume. The purpose of this work is to test the effectiveness of this approach in a virtual environment by investigating the precision of the posture and motion obtained with various numbers and arrangements of stationary cameras. An average 1% position error has been obtained with five cameras.