{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mvdream-multi-view-diffusion-for-3d","title":"MVDream: Multi-view Diffusion for 3D Generation","arxiv_id":"2308.16512","date":"2023-08-31","proceeding":null,"authors":["Yichun Shi","Peng Wang","Jianglong Ye","Mai Long","Kejie Li","Xiao Yang"],"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. 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