  Data-driven generative modeling has made remarkable progress by leveraging
the power of deep neural networks. A reoccurring challenge is how to enable a
model to generate a rich variety of samples from the entire target
distribution, rather than only from a distribution confined to the training
data. In other words, we would like the generative model to go beyond the
observed samples and learn to generate ``unseen'', yet still plausible, data.
In our work, we present CompoNet, a generative neural network for 2D or 3D
shapes that is based on a part-based prior, where the key idea is for the
network to synthesize shapes by varying both the shape parts and their
compositions. Treating a shape not as an unstructured whole, but as a
(re-)composable set of deformable parts, adds a combinatorial dimension to the
generative process to enrich the diversity of the output, encouraging the
generator to venture more into the ``unseen''. We show that our part-based
model generates richer variety of plausible shapes compared with baseline
generative models. To this end, we introduce two quantitative metrics to
evaluate the diversity of a generative model and assess how well the generated
data covers both the training data and unseen data from the same target
distribution. Code is available at https://github.com/nschor/CompoNet.
