title: MVDream: Multi-view Diffusion for 3D Generation
summary: We propose MVDream, a multi-view diffusion model that is able to generate
  geometrically consistent multi-view images from a given text prompt. By
  leveraging image diffusion models pre-trained on large-scale web datasets and a
  multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
  model can achieve both the generalizability of 2D diffusion and the consistency
  of 3D data. Such a model can thus be applied as a multi-view prior for 3D
  generation via Score Distillation Sampling, where it greatly improves the
  stability of existing 2D-lifting methods by solving the 3D consistency problem.
  Finally, we show that the multi-view diffusion model can also be fine-tuned
  under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
  application, where the consistency can be maintained after learning the subject
  identity.
paper_authors._id: 64f19b8099cfca31353b5e06
paper_authors.name: Yichun Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-01T10:18:25.618Z
paper_authors._id: 6309ad81b105f8675bd5a796
paper_authors.avatarUrl: /avatars/01c2186924486da4606e128e83709164.svg
paper_authors.fullname: Shi
paper_authors.type: user
paper_authors.user: Yichun
paper_authors._id: 64f19b8099cfca31353b5e07
paper_authors.name: Peng Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-01T17:00:38.224Z
paper_authors._id: 619c4a5cb392787f0f3ead53
paper_authors.avatarUrl: /avatars/e62bc6fed6e9cbe4e0339e050cc3be5f.svg
paper_authors.fullname: peng  wang
paper_authors.type: user
paper_authors.user: Peng-Wang
paper_authors._id: 64f19b8099cfca31353b5e08
paper_authors.name: Jianglong Ye
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-01T10:20:54.620Z
paper_authors._id: 634e2696d00bb5d92c3f4e86
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1674216878324-634e2696d00bb5d92c3f4e86.jpeg
paper_authors.fullname: Jianglong Ye
paper_authors.type: user
paper_authors.user: jianglongye
paper_authors._id: 64f19b8099cfca31353b5e09
paper_authors.name: Mai Long
paper_authors._id: 64f19b8099cfca31353b5e0a
paper_authors.name: Kejie Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-01T10:22:04.101Z
paper_authors._id: 640f4de8de9b00b4eb9cd698
paper_authors.avatarUrl: /avatars/4ecf573eb80f0389cc333ec2b6b26c0e.svg
paper_authors.fullname: Kejie Li
paper_authors.type: user
paper_authors.user: KejieLi
paper_authors._id: 64f19b8099cfca31353b5e0b
paper_authors.name: Xiao Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-04T07:32:20.581Z
paper_authors._id: 62b3d2490385d6048985b42b
paper_authors.avatarUrl: /avatars/1c4e534d086d53ef1057fbc00334350a.svg
paper_authors.fullname: Xiao Yang
paper_authors.type: user
paper_authors.user: seravee008
paper_title: MVDream: Multi-view Diffusion for 3D Generation
paper_summary: We propose MVDream, a multi-view diffusion model that is able to generate
  geometrically consistent multi-view images from a given text prompt. By
  leveraging image diffusion models pre-trained on large-scale web datasets and a
  multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
  model can achieve both the generalizability of 2D diffusion and the consistency
  of 3D data. Such a model can thus be applied as a multi-view prior for 3D
  generation via Score Distillation Sampling, where it greatly improves the
  stability of existing 2D-lifting methods by solving the 3D consistency problem.
  Finally, we show that the multi-view diffusion model can also be fine-tuned
  under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
  application, where the consistency can be maintained after learning the subject
  identity.
paper_ai_summary: MVDream generates geometrically consistent multi-view images from text prompts using pre-trained image diffusion models and Score Distillation Sampling, improving 3D generation stability and supporting personalized generation.
paper_ai_keywords: multi-view diffusion model text prompt image diffusion models large-scale web datasets multi-view dataset 3D assets Score Distillation Sampling 3D consistency problem fine-tuned few shot setting DreamBooth3D