The field of text-to-3D content generation has made significant progress in generating realistic 3D objects, with existing methodologies like Score Distillation Sampling (SDS) offering promising guidance. However, these methods often encounter the "Janus" problem-multi-face ambiguities due to imprecise guidance. Additionally, while recent advancements in 3D gaussian splitting have shown its efficacy in representing 3D volumes, optimization of this representation remains largely unexplored. This paper introduces a unified framework for text-to-3D content generation that addresses these critical gaps. Our approach utilizes multi-view guidance to iteratively form the structure of the 3D model, progressively enhancing detail and accuracy. We also introduce a novel densification algorithm that aligns gaussians close to the surface, optimizing the structural integrity and fidelity of the generated models. Extensive experiments validate our approach, demonstrating that it produces high-quality visual outputs with minimal time cost. Notably, our method achieves high-quality results within half an hour of training, offering a substantial efficiency gain over most existing methods, which require hours of training time to achieve comparable results.

本研究解决了文本到3D内容生成中的“贾努斯”问题和优化3D高斯分裂表示的不足，通过多视角指导逐步构建3D模型以提高细节和准确性。此外，研究引入了一种新颖的稠密化算法，优化了生成模型的结构完整性和保真度，实验结果证明该方法显著提升了生成效率，训练时间仅需半小时。

MVGaussian：具有多视角指导和表面稠密化的高保真文本到3D内容生成