CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
This addresses the problem of limited diversity in generative models for shape synthesis, though it appears incremental as it builds on part-based priors.
The paper tackles the challenge of enabling generative models to produce diverse, plausible samples beyond the training data by introducing CompoNet, a part-based generative neural network for 2D/3D shapes that varies shape parts and compositions, resulting in richer variety compared to baseline models.
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.