  We address the problem of estimating the shape of a person's head, defined as
the geometry of the complete head surface, from a video taken with a single
moving camera, and determining the alignment of the fitted 3D head for all
video frames, irrespective of the person's pose. 3D head reconstructions
commonly tend to focus on perfecting the face reconstruction, leaving the scalp
to a statistical approximation. Our goal is to reconstruct the head model of
each person to enable future mixed reality applications. To do this, we recover
a dense 3D reconstruction and camera information via structure-from-motion and
multi-view stereo. These are then used in a new two-stage fitting process to
recover the 3D head shape by iteratively fitting a 3D morphable model of the
head with the dense reconstruction in canonical space and fitting it to each
person's head, using both traditional facial landmarks and scalp features
extracted from the head's segmentation mask. Our approach recovers consistent
geometry for varying head shapes, from videos taken by different people, with
different smartphones, and in a variety of environments from living rooms to
outdoor spaces.
