Paper
MVDream: Multi-view Diffusion for 3D Generation
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 · Authors: Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, Xiao Yang · Published 2023-08-31 · arXiv:2308.16512
- Published
- 2023-08-31
- Authors
- 6
- arXiv
- 2308.16512
- Category
- cs.CV
Abstract
arXiv:2308.16512
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, Xiao Yang
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.