Core Concepts
Proposing the Hyper-3DG framework for high-quality 3D asset generation through innovative hypergraph refinement.
Abstract
The Hyper-3DG framework introduces a method named "3D Gaussian Generation via Hypergraph (Hyper-3DG)" to address challenges in 3D object generation from textual prompts. The framework leverages a mainflow and a critical module, the "Geometry and Texture Hypergraph Refiner (HGRefiner)," to capture high-order correlations within 3D objects. By refining 3D Gaussians through patch-level processing and hypergraph learning, the framework enhances the quality of generated 3D objects while maintaining computational efficiency. Experimental results demonstrate superior performance compared to state-of-the-art methods in terms of geometry, texture, and structural integrity.
Stats
arXiv:2403.09236v1 [cs.CV] 14 Mar 2024
Quotes
"Our proposed method significantly enhances the quality of 3D generation while incurring no additional computational overhead."
"Our HGRefiner adeptly establishes high-order correlations within the physical spatial space as well as the latent visual space of the 3D objects at the patch level."
"Extensive experimentation has shown that our proposed method significantly enhances the quality of 3D generation while incurring no additional computational overhead."