GaussianDreamer bridges 2D and 3D diffusion models to generate 3D content from text prompts. The 3D diffusion model provides priors for initialization while the 2D diffusion model enriches geometry and appearance. It uses an explicit and efficient 3D Gaussian splatting representation to achieve fast, high-quality 3D asset generation compared to implicit neural radiance field approaches.