  We introduce MeronymNet, a novel hierarchical approach for controllable,
part-based generation of multi-category objects using a single unified model.
We adopt a guided coarse-to-fine strategy involving semantically conditioned
generation of bounding box layouts, pixel-level part layouts and ultimately,
the object depictions themselves. We use Graph Convolutional Networks, Deep
Recurrent Networks along with custom-designed Conditional Variational
Autoencoders to enable flexible, diverse and category-aware generation of 2-D
objects in a controlled manner. The performance scores for generated objects
reflect MeronymNet's superior performance compared to multiple strong baselines
and ablative variants. We also showcase MeronymNet's suitability for
controllable object generation and interactive object editing at various levels
of structural and semantic granularity.
