  Learning 3D head priors from large 2D image collections is an important step
towards high-quality 3D-aware human modeling. A core requirement is an
efficient architecture that scales well to large-scale datasets and large image
resolutions. Unfortunately, existing 3D GANs struggle to scale to generate
samples at high resolutions due to their relatively slow train and render
speeds, and typically have to rely on 2D superresolution networks at the
expense of global 3D consistency. To address these challenges, we propose
Generative Gaussian Heads (GGHead), which adopts the recent 3D Gaussian
Splatting representation within a 3D GAN framework. To generate a 3D
representation, we employ a powerful 2D CNN generator to predict Gaussian
attributes in the UV space of a template head mesh. This way, GGHead exploits
the regularity of the template's UV layout, substantially facilitating the
challenging task of predicting an unstructured set of 3D Gaussians. We further
improve the geometric fidelity of the generated 3D representations with a novel
total variation loss on rendered UV coordinates. Intuitively, this
regularization encourages that neighboring rendered pixels should stem from
neighboring Gaussians in the template's UV space. Taken together, our pipeline
can efficiently generate 3D heads trained only from single-view 2D image
observations. Our proposed framework matches the quality of existing 3D head
GANs on FFHQ while being both substantially faster and fully 3D consistent. As
a result, we demonstrate real-time generation and rendering of high-quality
3D-consistent heads at $1024^2$ resolution for the first time.
