  Text-to-3D generation represents an exciting field that has seen rapid
advancements, facilitating the transformation of textual descriptions into
detailed 3D models. However, current progress often neglects the intricate
high-order correlation of geometry and texture within 3D objects, leading to
challenges such as over-smoothness, over-saturation and the Janus problem. In
this work, we propose a method named ``3D Gaussian Generation via Hypergraph
(Hyper-3DG)'', designed to capture the sophisticated high-order correlations
present within 3D objects. Our framework is anchored by a well-established
mainflow and an essential module, named ``Geometry and Texture Hypergraph
Refiner (HGRefiner)''. This module not only refines the representation of 3D
Gaussians but also accelerates the update process of these 3D Gaussians by
conducting the Patch-3DGS Hypergraph Learning on both explicit attributes and
latent visual features. Our framework allows for the production of finely
generated 3D objects within a cohesive optimization, effectively circumventing
degradation. Extensive experimentation has shown that our proposed method
significantly enhances the quality of 3D generation while incurring no
additional computational overhead for the underlying framework. (Project code:
https://github.com/yjhboy/Hyper3DG)
