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Generative models have shown great promise in synthesizing photorealistic 3Dobjects, but they require large amounts of training data. We introduce SinGRAF,a 3D-aware generative model that is trained with a few input images of a singlescene. Once trained, SinGRAF generates different realizations of this 3D scenethat preserve the appearance of the input while varying scene layout. For thispurpose, we build on recent progress in 3D GAN architectures and introduce anovel progressive-scale patch discrimination approach during training. Withseveral experiments, we demonstrate that the results produced by SinGRAFoutperform the closest related works in both quality and diversity by a largemargin.

SinGRAF: Learning a 3D Generative Radiance Field for a Single Scene