  We propose OPAL-Net, a novel hierarchical architecture for part-based layout
generation of objects from multiple categories using a single unified model. We
adopt a coarse-to-fine strategy involving semantically conditioned
autoregressive generation of bounding box layouts and pixel-level part layouts
for objects. We use Graph Convolutional Networks, Deep Recurrent Networks along
with custom-designed Conditional Variational Autoencoders to enable flexible,
diverse and category-aware generation of object layouts. We train OPAL-Net on
PASCAL-Parts dataset. The generated samples and corresponding evaluation scores
demonstrate the versatility of OPAL-Net compared to ablative variants and
baselines.
