MVDream: Multi-view Diffusion for 3D Generation

• URL: http://arxiv.org/abs/2308.16512v4
• Date: Thu, 18 Apr 2024 04:12:32 GMT
• Title: MVDream: Multi-view Diffusion for 3D Generation
• Authors: Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, Xiao Yang,
• Abstract summary: 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.
• Score: 14.106283556521962
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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