GaussianDreamer: Fast Generation from Text to 3D Gaussian Splatting with
  Point Cloud Priors
Authors: name: Taoran Yi, name: Jiemin Fang, name: Guanjun Wu, name: Lingxi Xie, name: Xiaopeng Zhang, name: Wenyu Liu, name: Qi Tian, name: Xinggang Wang

In recent times, the generation of 3D assets from text prompts has shown
impressive results. Both 2D and 3D diffusion models can generate decent 3D
objects based on prompts. 3D diffusion models have good 3D consistency, but
their quality and generalization are limited as trainable 3D data is expensive
and hard to obtain. 2D diffusion models enjoy strong abilities of
generalization and fine generation, but the 3D consistency is hard to
guarantee. This paper attempts to bridge the power from the two types of
diffusion models via the recent explicit and efficient 3D Gaussian splatting
representation. A fast 3D generation framework, named as \name, is proposed,
where the 3D diffusion model provides point cloud priors for initialization and
the 2D diffusion model enriches the geometry and appearance. Operations of
noisy point growing and color perturbation are introduced to enhance the
initialized Gaussians. Our \name can generate a high-quality 3D instance within
25 minutes on one GPU, much faster than previous methods, while the generated
instances can be directly rendered in real time. Demos and code are available
at https://taoranyi.com/gaussiandreamer/.