  We present a generative method to estimate 3D human motion and body shape
from monocular video. Under the assumption that starting from an initial pose
optical flow constrains subsequent human motion, we exploit flow to find
temporally coherent human poses of a motion sequence. We estimate human motion
by minimizing the difference between computed flow fields and the output of an
artificial flow renderer. A single initialization step is required to estimate
motion over multiple frames. Several regularization functions enhance
robustness over time. Our test scenarios demonstrate that optical flow
effectively regularizes the under-constrained problem of human shape and motion
estimation from monocular video.
