As I see it, this breaks down into a few steps, each of which is non-trivial: A) From a set of 2d images, construct 3d models of a person in that pose. Map the

As I see it, this breaks down into a few steps, each of which is non-trivial: A) From a set of 2d images, construct 3d models of a person in that pose. Map the original image pixels onto that model (as a texture?) B) Rotate those models in 3d space to look for coincidence, and work backwards to figure out the respective angles, and merge the 3d models. C) Iterate to improve the solution -- Much as the SIFT method for generating scale free "interesting points" for 2d images enabled the panorama stitching software of the past decade, this enables a new software space that should be quite interesting.