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3D-aware GANs aim to synthesize realistic 3D scenes such that they can berendered in arbitrary perspectives to produce images. Although previous methodsproduce realistic images, they suffer from unstable training or degeneratesolutions where the 3D geometry is unnatural. We hypothesize that the 3Dgeometry is underdetermined due to the insufficient constraint, i.e., beingclassified as real image to the discriminator is not enough. To solve thisproblem, we propose to approximate the background as a spherical surface andrepresent a scene as a union of the foreground placed in the sphere and thethin spherical background. It reduces the degree of freedom in the backgroundfield. Accordingly, we modify the volume rendering equation and incorporatededicated constraints to design a novel 3D-aware GAN framework named BallGAN.BallGAN has multiple advantages as follows. 1) It produces more reasonable 3Dgeometry; the images of a scene across different viewpoints have betterphotometric consistency and fidelity than the state-of-the-art methods. 2) Thetraining becomes much more stable. 3) The foreground can be separately renderedon top of different arbitrary backgrounds.

BallGAN: 3D-aware Image Synthesis with a Spherical Background