Human Motion Capture is a widely used technique to obtain motion data
for animation of virtual characters. Commercial optical motion capture
systems are marker-based. This book is about marker-free motion capture
and its possibilities to acquire motion from a single viewing direction.
The focus of this book is on the optimization framework, which can be
applied to every pose estimation problem of articulated objects. The
motion function is formed with a combination of kinematic chains. This
formulation leads to a Nonlinear Optimization problem and is solved with
gradient-based methods, which are compared with respect to their
efficiency. A new contribution is the inclusion of second order motion
derivatives within the pose estimation. The pose estimation step
requires correspondences between known model of the person and observed
data. Computer Vision techniques are used to combine multiple types of
correspondences, which are used simultaneously in the estimation without
making approximations to the motion or optimization function, namely
3D-3D correspondences from stereo algorithms and 3D-2D correspondences
from image silhouettes and 2D point tracking.