MVDream: Multi-view Diffusion for 3D Generation

Autor:	Shi, Yichun, Wang, Peng, Ye, Jianglong, Long, Mai, Li, Kejie, Yang, Xiao
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: Reorganized for arXiv; Our project page is https://MV-Dream.github.io
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.16512 Zobrazit plný text záznamu View this record from Arxiv