Title: Semantically Controllable Generation of Physical Scenes with Explicit Knowledge. Abstract: Deep Generative Models (DGMs) are known for their superior capability in generating realistic data. Extending purely data-driven approaches, recent specialized DGMs satisfy additional controllable requirements such as embedding a traffic sign in a driving scene by manipulating patterns implicitly in the neuron or feature level. In this paper, we introduce a novel method to incorporate domain knowledge explicitly in the generation process to achieve the semantically controllable generation of physical scenes. We first categorize our knowledge into two types, the property of objects and the relationship among objects, to be consistent with the composition of natural scenes. We then propose a tree-structured generative model to learn hierarchical scene representation, whose nodes and edges naturally corresponded to the two types of knowledge, respectively. Consequently, explicit knowledge integration enables semantically controllable generation by imposing semantic rules on the properties of nodes and edges in the tree structure. We construct a synthetic example to illustrate the controllability and explainability of our method in a succinct setting. We further extend the synthetic example to realistic environments for autonomous vehicles and conduct extensive experiments: our method efficiently identifies adversarial physical scenes against different state-of-the-art 3D point cloud segmentation models and satisfies the traffic rules specified as the explicit knowledge.
