CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting
Alexander Vilesov Pradyumna Chari Achuta Kadambi
With the onset of diffusion-based generative models and their ability to
generate text-conditioned images, content generation has received a massive
invigoration. Recently, these models have been shown to provide useful guidance
for the generation of 3D graphics assets. However, existing work in
text-conditioned 3D generation faces fundamental constraints: (i) inability to
generate detailed, multi-object scenes, (ii) inability to textually control
multi-object configurations, and (iii) physically realistic scene composition.
In this work, we propose CG3D, a method for compositionally generating scalable
3D assets that resolves these constraints. We find that explicit Gaussian
radiance fields, parameterized to allow for compositions of objects, possess
the capability to enable semantically and physically consistent scenes. By
utilizing a guidance framework built around this explicit representation, we
show state of the art results, capable of even exceeding the guiding diffusion
model in terms of object combinations and physics accuracy.