  Text-to-3D generation by distilling pretrained large-scale text-to-image
diffusion models has shown great promise but still suffers from inconsistent 3D
geometric structures (Janus problems) and severe artifacts. The aforementioned
problems mainly stem from 2D diffusion models lacking 3D awareness during the
lifting. In this work, we present GeoDream, a novel method that incorporates
explicit generalized 3D priors with 2D diffusion priors to enhance the
capability of obtaining unambiguous 3D consistent geometric structures without
sacrificing diversity or fidelity. Specifically, we first utilize a multi-view
diffusion model to generate posed images and then construct cost volume from
the predicted image, which serves as native 3D geometric priors, ensuring
spatial consistency in 3D space. Subsequently, we further propose to harness 3D
geometric priors to unlock the great potential of 3D awareness in 2D diffusion
priors via a disentangled design. Notably, disentangling 2D and 3D priors
allows us to refine 3D geometric priors further. We justify that the refined 3D
geometric priors aid in the 3D-aware capability of 2D diffusion priors, which
in turn provides superior guidance for the refinement of 3D geometric priors.
Our numerical and visual comparisons demonstrate that GeoDream generates more
3D consistent textured meshes with high-resolution realistic renderings (i.e.,
1024 $\times$ 1024) and adheres more closely to semantic coherence.
