Human motion capture typically requires several high quality, synchronized and calibrated cameras in a studio environment and can be potentially costly and technically complex. Instead, we propose a system which combines and improves upon two existing techniques, yielding an efficient method that recovers maximum likelihood joint angles and anthropomorphic data of the subject by factorization.

The first technique concerns using a rank constraint framework to synchronize sequences of non-rigid motions where we extend affine methods to perspective and homography projection models. The second is a self-calibration method for two affine cameras, using constraints derived from prior knowledge of the underlying structure. We propose a minimal parameterization of the system to obtain an initial solution then apply a full bundle adjustment over the free parameters based on a geometric error.

We demonstrate the efficacy of our method by comparing the recovered structure and motion with that from a commercial motion capture system.