Title: Deep Structured Generative Models,
Abstract: Deep generative models have shown promising results in generating realistic
images, but it is still non-trivial to generate images with complicated
structures. The main reason is that most of the current generative models fail
to explore the structures in the images including spatial layout and semantic
relations between objects. To address this issue, we propose a novel deep
structured generative model which boosts generative adversarial networks (GANs)
with the aid of structure information. In particular, the layout or structure
of the scene is encoded by a stochastic and-or graph (sAOG), in which the
terminal nodes represent single objects and edges represent relations between
objects. With the sAOG appropriately harnessed, our model can successfully
capture the intrinsic structure in the scenes and generate images of
complicated scenes accordingly. Furthermore, a detection network is introduced
to infer scene structures from a image. Experimental results demonstrate the
effectiveness of our proposed method on both modeling the intrinsic structures,
and generating realistic images.