  Procedural models are being widely used to synthesize scenes for graphics,
gaming, and to create (labeled) synthetic datasets for ML. In order to produce
realistic and diverse scenes, a number of parameters governing the procedural
models have to be carefully tuned by experts. These parameters control both the
structure of scenes being generated (e.g. how many cars in the scene), as well
as parameters which place objects in valid configurations. Meta-Sim aimed at
automatically tuning parameters given a target collection of real images in an
unsupervised way. In Meta-Sim2, we aim to learn the scene structure in addition
to parameters, which is a challenging problem due to its discrete nature.
Meta-Sim2 proceeds by learning to sequentially sample rule expansions from a
given probabilistic scene grammar. Due to the discrete nature of the problem,
we use Reinforcement Learning to train our model, and design a feature space
divergence between our synthesized and target images that is key to successful
training. Experiments on a real driving dataset show that, without any
supervision, we can successfully learn to generate data that captures discrete
structural statistics of objects, such as their frequency, in real images. We
also show that this leads to downstream improvement in the performance of an
object detector trained on our generated dataset as opposed to other baseline
simulation methods. Project page:
https://nv-tlabs.github.io/meta-sim-structure/.
