  We introduce SDM-NET, a deep generative neural network which produces
structured deformable meshes. Specifically, the network is trained to generate
a spatial arrangement of closed, deformable mesh parts, which respect the
global part structure of a shape collection, e.g., chairs, airplanes, etc. Our
key observation is that while the overall structure of a 3D shape can be
complex, the shape can usually be decomposed into a set of parts, each
homeomorphic to a box, and the finer-scale geometry of the part can be
recovered by deforming the box. The architecture of SDM-NET is that of a
two-level variational autoencoder (VAE). At the part level, a PartVAE learns a
deformable model of part geometries. At the structural level, we train a
Structured Parts VAE (SP-VAE), which jointly learns the part structure of a
shape collection and the part geometries, ensuring a coherence between global
shape structure and surface details. Through extensive experiments and
comparisons with the state-of-the-art deep generative models of shapes, we
demonstrate the superiority of SDM-NET in generating meshes with visual
quality, flexible topology, and meaningful structures, which benefit shape
interpolation and other subsequently modeling tasks.
