Real-time human pose inference using kernel principal component pre-image approximations

Abstract

We present a real-time markerless human motion capture technique based on un-calibrated synchronized cameras. Training sets of real motions captured from marker based systems are used to learn an optimal pose manifold of human motion via Kernel Principal Component Analysis (KPCA). Similarly, a synthetic silhouette manifold is also learnt, and markerless motion capture can then be viewed as the problem of mapping from the silhouette manifold to the pose manifold. After training, novel silhouettes of previously unseen actors are projected through the two manifolds using Locally Linear Embedding (LLE) reconstruction. The output pose is generated by approximating the pre-image (inverse mapping) of the LLE reconstructed vector from the pose manifold.