title: SyncDreamer: Generating Multiview-consistent Images from a Single-view
    Image
summary: In this paper, we present a novel diffusion model called that generates
  multiview-consistent images from a single-view image. Using pretrained
  large-scale 2D diffusion models, recent work Zero123 demonstrates the ability
  to generate plausible novel views from a single-view image of an object.
  However, maintaining consistency in geometry and colors for the generated
  images remains a challenge. To address this issue, we propose a synchronized
  multiview diffusion model that models the joint probability distribution of
  multiview images, enabling the generation of multiview-consistent images in a
  single reverse process. SyncDreamer synchronizes the intermediate states of all
  the generated images at every step of the reverse process through a 3D-aware
  feature attention mechanism that correlates the corresponding features across
  different views. Experiments show that SyncDreamer generates images with high
  consistency across different views, thus making it well-suited for various 3D
  generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.
paper_authors._id: 64faa342d75293f417c44529
paper_authors.name: Yuan Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-06T07:05:44.865Z
paper_authors._id: 63f5dc824b831cc179b90e8f
paper_authors.avatarUrl: /avatars/2c2d22c6b605a9c412a9c37960efeeb2.svg
paper_authors.fullname: Yuan Liu
paper_authors.type: user
paper_authors.user: liuyuan-pal
paper_authors._id: 64faa342d75293f417c4452a
paper_authors.name: Cheng Lin
paper_authors._id: 64faa342d75293f417c4452b
paper_authors.name: Zijiao Zeng
paper_authors._id: 64faa342d75293f417c4452c
paper_authors.name: Xiaoxiao Long
paper_authors._id: 64faa342d75293f417c4452d
paper_authors.name: Lingjie Liu
paper_authors._id: 64faa342d75293f417c4452e
paper_authors.name: Taku Komura
paper_authors._id: 64faa342d75293f417c4452f
paper_authors.name: Wenping Wang
paper_title: SyncDreamer: Generating Multiview-consistent Images from a Single-view
    Image
paper_summary: In this paper, we present a novel diffusion model called that generates
  multiview-consistent images from a single-view image. Using pretrained
  large-scale 2D diffusion models, recent work Zero123 demonstrates the ability
  to generate plausible novel views from a single-view image of an object.
  However, maintaining consistency in geometry and colors for the generated
  images remains a challenge. To address this issue, we propose a synchronized
  multiview diffusion model that models the joint probability distribution of
  multiview images, enabling the generation of multiview-consistent images in a
  single reverse process. SyncDreamer synchronizes the intermediate states of all
  the generated images at every step of the reverse process through a 3D-aware
  feature attention mechanism that correlates the corresponding features across
  different views. Experiments show that SyncDreamer generates images with high
  consistency across different views, thus making it well-suited for various 3D
  generation tasks such as novel-view-synthesis, text-to-3D, and image-to-3D.
paper_ai_summary: SyncDreamer generates multiview-consistent images using a synchronized diffusion model and 3D-aware feature attention mechanism, improving the quality of 3D generation tasks.
paper_ai_keywords: diffusion model multiview-consistent images pretrained large-scale 2D diffusion models synchronized multiview diffusion model joint probability distribution 3D-aware feature attention mechanism multiview images reverse process novel-view-synthesis text-to-3D image-to-3D