  Reconstruction of the shape and motion of humans from RGB-D is a challenging
problem, receiving much attention in recent years. Recent approaches for
full-body reconstruction use a statistic shape model, which is built upon
accurate full-body scans of people in skin-tight clothes, to complete invisible
parts due to occlusion. Such a statistic model may still be fit to an RGB-D
measurement with loose clothes but cannot describe its deformations, such as
clothing wrinkles. Observed surfaces may be reconstructed precisely from actual
measurements, while we have no cues for unobserved surfaces. For full-body
reconstruction with loose clothes, we propose to use lower dimensional
embeddings of texture and deformation referred to as eigen-texturing and
eigen-deformation, to reproduce views of even unobserved surfaces. Provided a
full-body reconstruction from a sequence of partial measurements as 3D meshes,
the texture and deformation of each triangle are then embedded using
eigen-decomposition. Combined with neural-network-based coefficient regression,
our method synthesizes the texture and deformation from arbitrary viewpoints.
We evaluate our method using simulated data and visually demonstrate how our
method works on real data.
