  3D-aware generative models have shown that the introduction of 3D information
can lead to more controllable image generation. In particular, the current
state-of-the-art model GIRAFFE can control each object's rotation, translation,
scale, and scene camera pose without corresponding supervision. However,
GIRAFFE only operates well when the image resolution is low. We propose GIRAFFE
HD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE's
controllable features while generating high-quality, high-resolution images
($512^2$ resolution and above). The key idea is to leverage a style-based
neural renderer, and to independently generate the foreground and background to
force their disentanglement while imposing consistency constraints to stitch
them together to composite a coherent final image. We demonstrate
state-of-the-art 3D controllable high-resolution image generation on multiple
natural image datasets.
