  We present SAGNet, a structure-aware generative model for 3D shapes. Given a
set of segmented objects of a certain class, the geometry of their parts and
the pairwise relationships between them (the structure) are jointly learned and
embedded in a latent space by an autoencoder. The encoder intertwines the
geometry and structure features into a single latent code, while the decoder
disentangles the features and reconstructs the geometry and structure of the 3D
model. Our autoencoder consists of two branches, one for the structure and one
for the geometry. The key idea is that during the analysis, the two branches
exchange information between them, thereby learning the dependencies between
structure and geometry and encoding two augmented features, which are then
fused into a single latent code. This explicit intertwining of information
enables separately controlling the geometry and the structure of the generated
models. We evaluate the performance of our method and conduct an ablation
study. We explicitly show that encoding of shapes accounts for both
similarities in structure and geometry. A variety of quality results generated
by SAGNet are presented. The data and code are at
https://github.com/zhijieW-94/SAGNet.

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