  We present an approach to generating 3D human models from images. The key to
our framework is that we predict double-sided orthographic depth maps and color
images from a single perspective projected image. Our framework consists of
three networks. The first network predicts normal maps to recover geometric
details such as wrinkles in the clothes and facial regions. The second network
predicts shade-removed images for the front and back views by utilizing the
predicted normal maps. The last multi-headed network takes both normal maps and
shade-free images and predicts depth maps while selectively fusing photometric
and geometric information through multi-headed attention gates. Experimental
results demonstrate that our method shows visually plausible results and
competitive performance in terms of various evaluation metrics over
state-of-the-art methods.
