  Voronoi diagrams are highly compact representations that are used in various
Graphics applications. In this work, we show how to embed a differentiable
version of it -- via a novel deep architecture -- into a generative deep
network. By doing so, we achieve a highly compact latent embedding that is able
to provide much more detailed reconstructions, both in 2D and 3D, for various
shapes. In this tech report, we introduce our representation and present a set
of preliminary results comparing it with recently proposed implicit occupancy
networks.
