Text-to-3D using Gaussian Splatting
Authors: name: Zilong Chen, name: Feng Wang, name: Huaping Liu

In this paper, we present Gaussian Splatting based text-to-3D generation
(GSGEN), a novel approach for generating high-quality 3D objects. Previous
methods suffer from inaccurate geometry and limited fidelity due to the absence
of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a
recent state-of-the-art representation, to address existing shortcomings by
exploiting the explicit nature 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 a 3D
geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and
3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an
iterative refinement to enrich 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 content with
delicate details and more accurate geometry. Extensive evaluations demonstrate
the effectiveness of our method, especially for capturing high-frequency
components. Video results are provided at https://gsgen3d.github.io. Our code
is available at https://github.com/gsgen3d/gsgen