  We introduce MVDream, a diffusion model that is able to generate consistent
multi-view images from a given text prompt. Learning from both 2D and 3D data,
a multi-view diffusion model can achieve the generalizability of 2D diffusion
models and the consistency of 3D renderings. We demonstrate that such a
multi-view diffusion model is implicitly a generalizable 3D prior agnostic to
3D representations. It can be applied to 3D generation via Score Distillation
Sampling, significantly enhancing the consistency and stability of existing
2D-lifting methods. It can also learn new concepts from a few 2D examples, akin
to DreamBooth, but for 3D generation.
