  3D shape generation is a challenging problem due to the high-dimensional
output space and complex part configurations of real-world objects. As a
result, existing algorithms experience difficulties in accurate generative
modeling of 3D shapes. Here, we propose a novel factorized generative model for
3D shape generation that sequentially transitions from coarse to fine scale
shape generation. To this end, we introduce an unsupervised primitive discovery
algorithm based on a higher-order conditional random field model. Using the
primitive parts for shapes as attributes, a parameterized 3D representation is
modeled in the first stage. This representation is further refined in the next
stage by adding fine scale details to shape. Our results demonstrate improved
representation ability of the generative model and better quality samples of
newly generated 3D shapes. Further, our primitive generation approach can
accurately parse common objects into a simplified representation.
