  In this paper, we propose a novel approach to reconstruct 3D human body
shapes based on a sparse set of RGBD frames using a single RGBD camera. We
specifically focus on the realistic settings where human subjects move freely
during the capture. The main challenge is how to robustly fuse these sparse
frames into a canonical 3D model, under pose changes and surface occlusions.
This is addressed by our new framework consisting of the following steps.
First, based on a generative human template, for every two frames having
sufficient overlap, an initial pairwise alignment is performed; It is followed
by a global non-rigid registration procedure, in which partial results from
RGBD frames are collected into a unified 3D shape, under the guidance of
correspondences from the pairwise alignment; Finally, the texture map of the
reconstructed human model is optimized to deliver a clear and spatially
consistent texture. Empirical evaluations on synthetic and real datasets
demonstrate both quantitatively and qualitatively the superior performance of
our framework in reconstructing complete 3D human models with high fidelity. It
is worth noting that our framework is flexible, with potential applications
going beyond shape reconstruction. As an example, we showcase its use in
reshaping and reposing to a new avatar.
