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With the onset of diffusion-based generative models and their ability togenerate text-conditioned images, content generation has received a massiveinvigoration. Recently, these models have been shown to provide useful guidancefor the generation of 3D graphics assets. However, existing work intext-conditioned 3D generation faces fundamental constraints: (i) inability togenerate detailed, multi-object scenes, (ii) inability to textually controlmulti-object configurations, and (iii) physically realistic scene composition.In this work, we propose CG3D, a method for compositionally generating scalable3D assets that resolves these constraints. We find that explicit Gaussianradiance fields, parameterized to allow for compositions of objects, possessthe capability to enable semantically and physically consistent scenes. Byutilizing a guidance framework built around this explicit representation, weshow state of the art results, capable of even exceeding the guiding diffusionmodel in terms of object combinations and physics accuracy.

CG3D: Compositional Generation for Text-to-3D via Gaussian Splatting