  Advances in 3D-aware generative models have pushed the boundary of image
synthesis with explicit camera control. To achieve high-resolution image
synthesis, several attempts have been made to design efficient generators, such
as hybrid architectures with both 3D and 2D components. However, such a design
compromises multiview consistency, and the design of a pure 3D generator with
high resolution is still an open problem. In this work, we present Generative
Volumetric Primitives (GVP), the first pure 3D generative model that can sample
and render 512-resolution images in real-time. GVP jointly models a number of
volumetric primitives and their spatial information, both of which can be
efficiently generated via a 2D convolutional network. The mixture of these
primitives naturally captures the sparsity and correspondence in the 3D volume.
The training of such a generator with a high degree of freedom is made possible
through a knowledge distillation technique. Experiments on several datasets
demonstrate superior efficiency and 3D consistency of GVP over the
state-of-the-art.
