  In this work, we present an automated workflow to bring human figures, one of
the most frequently appearing entities on pictorial maps, to the third
dimension. Our workflow is based on training data and neural networks for
single-view 3D reconstruction of real humans from photos. We first let a
network consisting of fully connected layers estimate the depth coordinate of
2D pose points. The gained 3D pose points are inputted together with 2D masks
of body parts into a deep implicit surface network to infer 3D signed distance
fields (SDFs). By assembling all body parts, we derive 2D depth images and body
part masks of the whole figure for different views, which are fed into a fully
convolutional network to predict UV images. These UV images and the texture for
the given perspective are inserted into a generative network to inpaint the
textures for the other views. The textures are enhanced by a cartoonization
network and facial details are resynthesized by an autoencoder. Finally, the
generated textures are assigned to the inferred body parts in a ray marcher. We
test our workflow with 12 pictorial human figures after having validated
several network configurations. The created 3D models look generally promising,
especially when considering the challenges of silhouette-based 3D recovery and
real-time rendering of the implicit SDFs. Further improvement is needed to
reduce gaps between the body parts and to add pictorial details to the
textures. Overall, the constructed figures may be used for animation and
storytelling in digital 3D maps.
