  We present a simple yet effective general-purpose framework for modeling 3D
shapes by leveraging recent advances in 2D image generation using CNNs. Using
just a single depth image of the object, we can output a dense multi-view depth
map representation of 3D objects. Our simple encoder-decoder framework,
comprised of a novel identity encoder and class-conditional viewpoint
generator, generates 3D consistent depth maps. Our experimental results
demonstrate the two-fold advantage of our approach. First, we can directly
borrow architectures that work well in the 2D image domain to 3D. Second, we
can effectively generate high-resolution 3D shapes with low computational
memory. Our quantitative evaluations show that our method is superior to
existing depth map methods for reconstructing and synthesizing 3D objects and
is competitive with other representations, such as point clouds, voxel grids,
and implicit functions.
