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In this work, we introduce CC3D, a conditional generative model that
synthesizes complex 3D scenes conditioned on 2D semantic scene layouts, trained
using single-view images. Different from most existing 3D GANs that limit their
applicability to aligned single objects, we focus on generating complex scenes
with multiple objects, by modeling the compositional nature of 3D scenes. By
devising a 2D layout-based approach for 3D synthesis and implementing a new 3D
field representation with a stronger geometric inductive bias, we have created
a 3D GAN that is both efficient and of high quality, while allowing for a more
controllable generation process. Our evaluations on synthetic 3D-FRONT and
real-world KITTI-360 datasets demonstrate that our model generates scenes of
improved visual and geometric quality in comparison to previous works.
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