Bridging Geometry-Coherent Text-to-3D Generation with Multi-View Diffusion Priors and Gaussian Splatting

TL;DR

This paper introduces Coupled Score Distillation (CSD), a novel framework that improves text-to-3D generation by ensuring multi-view geometric consistency and directly optimizing 3D Gaussian Splatting, resulting in high-quality 3D assets.

Contribution

The paper presents CSD, a new multi-view joint distribution prior coupling method that enhances geometric consistency in text-to-3D generation and enables direct optimization of 3D Gaussian Splatting.

Findings

CSD effectively couples multi-view priors for consistent 3D generation.

The approach produces high-quality, geometrically consistent 3D assets.

Experimental results show efficiency and competitive quality.

Abstract

Score Distillation Sampling (SDS) leverages pretrained 2D diffusion models to advance text-to-3D generation but neglects multi-view correlations, being prone to geometric inconsistencies and multi-face artifacts in the generated 3D content. In this work, we propose Coupled Score Distillation (CSD), a framework that couples multi-view joint distribution priors to ensure geometrically consistent 3D generation while enabling the stable and direct optimization of 3D Gaussian Splatting. Specifically, by reformulating the optimization as a multi-view joint optimization problem, we derive an effective optimization rule that effectively couples multi-view priors to guide optimization across different viewpoints while preserving the diversity of generated 3D assets. Additionally, we propose a framework that directly optimizes 3D Gaussian Splatting (3D-GS) with random initialization to generate geometrically consistent 3D content. We further employ a deformable tetrahedral grid, initialized from 3D-GS and refined through CSD, to produce high-quality, refined meshes. Quantitative and qualitative experimental results demonstrate the efficiency and competitive quality of our approach.