  Recovering textured 3D models of non-rigid human body shapes is challenging
due to self-occlusions caused by complex body poses and shapes, clothing
obstructions, lack of surface texture, background clutter, sparse set of
cameras with non-overlapping fields of view, etc. Further, a calibration-free
environment adds additional complexity to both - reconstruction and texture
recovery. In this paper, we propose a deep learning based solution for textured
3D reconstruction of human body shapes from a single view RGB image. This is
achieved by first recovering the volumetric grid of the non-rigid human body
given a single view RGB image followed by orthographic texture view synthesis
using the respective depth projection of the reconstructed (volumetric) shape
and input RGB image. We propose to co-learn the depth information readily
available with affordable RGBD sensors (e.g., Kinect) while showing multiple
views of the same object during the training phase. We show superior
reconstruction performance in terms of quantitative and qualitative results, on
both, publicly available datasets (by simulating the depth channel with virtual
Kinect) as well as real RGBD data collected with our calibrated multi Kinect
setup.
