  We present a novel framework that learns to predict human anatomy from body
surface. Specifically, our approach generates a synthetic X-ray image of a
person only from the person's surface geometry. Furthermore, the synthetic
X-ray image is parametrized and can be manipulated by adjusting a set of body
markers which are also generated during the X-ray image prediction. With the
proposed framework, multiple synthetic X-ray images can easily be generated by
varying surface geometry. By perturbing the parameters, several additional
synthetic X-ray images can be generated from the same surface geometry. As a
result, our approach offers a potential to overcome the training data barrier
in the medical domain. This capability is achieved by learning a pair of
networks - one learns to generate the full image from the partial image and a
set of parameters, and the other learns to estimate the parameters given the
full image. During training, the two networks are trained iteratively such that
they would converge to a solution where the predicted parameters and the full
image are consistent with each other. In addition to medical data enrichment,
our framework can also be used for image completion as well as anomaly
detection.
