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.
在本文中，我们提出了基于高斯溅射的文本到3D生成方法（GSGEN），这是一种生成高质量3D对象的新颖方法。以前的方法由于缺乏3D先验和适当的表示，而受到几何精度不准确和保真度有限的困扰。我们利用3D高斯溅射——一种最新的顶尖表示法，通过利用其明确性质来解决现有的不足，使得能够结合3D先验。具体来说，我们的方法采用了一个渐进的优化策略，包括一个几何优化阶段和一个外观细化阶段。在几何优化中，根据3D几何先验以及常规的2D SDS损失，建立了一个粗略的表示，确保了一个合理且与3D一致的粗略形状。随后，获得的高斯进行迭代细化以丰富细节。在这个阶段，我们通过基于紧凑性的增密来增加高斯数量，以提高连续性和改善保真度。通过这些设计，我们的方法能够生成具有精致细节和更准确几何形状的3D内容。广泛的评估证明了我们方法的有效性，尤其是在捕捉高频组件方面。