Recent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practical usage. In this paper, we propose DreamGaussian, a novel 3D content generation framework that achieves both efficiency and quality simultaneously. Our key insight is to design a generative 3D Gaussian Splatting model with companioned mesh extraction and texture refinement in UV space. In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks. To further enhance the texture quality and facilitate downstream applications, we introduce an efficient algorithm to convert 3D Gaussians into textured meshes and apply a fine-tuning stage to refine the details. Extensive experiments demonstrate the superior efficiency and competitive generation quality of our proposed approach. Notably, DreamGaussian produces high-quality textured meshes in just 2 minutes from a single-view image, achieving approximately 10 times acceleration compared to existing methods.

翻译：近期三维内容创建的进展主要依赖于基于优化的分数蒸馏采样（SDS）方法。尽管这些方法已展现出令人鼓舞的成果，但通常存在逐样本优化速度慢的问题，限制了其实用性。本文提出DreamGaussian——一种兼顾效率与质量的新型三维内容生成框架。核心思路是设计生成式三维高斯泼溅模型，并在UV空间中配合网格提取与纹理细化。与神经辐射场中使用的占用剪枝不同，我们证明渐进式三维高斯致密化在三维生成任务中收敛速度显著更快。为提升纹理质量并便于下游应用，我们引入高效算法将三维高斯转换为带纹理网格，并通过微调阶段细化细节。大量实验表明，本方法具有优越效率和竞争力的生成质量。值得注意的是，DreamGaussian仅需2分钟即可从单视角图像生成高质量带纹理网格，相比现有方法实现约10倍加速。