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3D-aware generative models have shown that the introduction of 3D informationcan lead to more controllable image generation. In particular, the currentstate-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 GIRAFFEHD, a high-resolution 3D-aware generative model that inherits all of GIRAFFE'scontrollable features while generating high-quality, high-resolution images($512^2$ resolution and above). The key idea is to leverage a style-basedneural renderer, and to independently generate the foreground and background toforce their disentanglement while imposing consistency constraints to stitchthem together to composite a coherent final image. We demonstratestate-of-the-art 3D controllable high-resolution image generation on multiplenatural image datasets.

GIRAFFE HD: A High-Resolution 3D-aware Generative Model