Text-to-3D using Gaussian Splatting

Zilong Chen, Feng Wang, Yikai Wang, Huaping Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 21401-21412

Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the Janus issue since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response this paper proposes GSGEN a novel method that adopts Gaussian Splatting a recent state-of-the-art representation to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically our method adopts a progressive optimization strategy which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization ensuring a sensible and 3D-consistent rough shape. Subsequently the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method especially for capturing high-frequency components.