title: An Object is Worth 64x64 Pixels: Generating 3D Object via Image
    Diffusion
summary: We introduce a new approach for generating realistic 3D models with UV maps
  through a representation termed "Object Images." This approach encapsulates
  surface geometry, appearance, and patch structures within a 64x64 pixel image,
  effectively converting complex 3D shapes into a more manageable 2D format. By
  doing so, we address the challenges of both geometric and semantic irregularity
  inherent in polygonal meshes. This method allows us to use image generation
  models, such as Diffusion Transformers, directly for 3D shape generation.
  Evaluated on the ABO dataset, our generated shapes with patch structures
  achieve point cloud FID comparable to recent 3D generative models, while
  naturally supporting PBR material generation.
paper_authors._id: 66b2dccffa7069c7f1f22f12
paper_authors.name: Xingguang Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:14:00.289Z
paper_authors._id: 6441ccbf608bf7e3ad7941f1
paper_authors.avatarUrl: /avatars/55f83f3bd1e49d0d74cc949cae54a57b.svg
paper_authors.fullname: Xingguang Yan
paper_authors.type: user
paper_authors.user: yanxg
paper_authors._id: 66b2dccffa7069c7f1f22f13
paper_authors.name: Han-Hung Lee
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:14:06.434Z
paper_authors._id: 6313c7a754e6e5d9f0fa4d81
paper_authors.avatarUrl: /avatars/0b0e5f09171b87d61260225b2021aa98.svg
paper_authors.fullname: HAN-HUNG LEE
paper_authors.type: user
paper_authors.user: rexleeppp
paper_authors._id: 66b2dccffa7069c7f1f22f14
paper_authors.name: Ziyu Wan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:14:18.316Z
paper_authors._id: 669f5261ab5548b23ea659f0
paper_authors.avatarUrl: /avatars/9db59c5db1c451e492081e1bdaede0c8.svg
paper_authors.fullname: wanziyu
paper_authors.type: user
paper_authors.user: wnaziyu002
paper_authors._id: 66b2dccffa7069c7f1f22f15
paper_authors.name: Angel X. Chang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-08T12:14:28.715Z
paper_authors._id: 6363d5d92e11f41bc819176e
paper_authors.avatarUrl: /avatars/516bea7f7da973bfcc992df9a63727ca.svg
paper_authors.fullname: Angel Chang
paper_authors.type: user
paper_authors.user: angelxuanchang
paper_title: An Object is Worth 64x64 Pixels: Generating 3D Object via Image
    Diffusion
paper_summary: We introduce a new approach for generating realistic 3D models with UV maps
  through a representation termed "Object Images." This approach encapsulates
  surface geometry, appearance, and patch structures within a 64x64 pixel image,
  effectively converting complex 3D shapes into a more manageable 2D format. By
  doing so, we address the challenges of both geometric and semantic irregularity
  inherent in polygonal meshes. This method allows us to use image generation
  models, such as Diffusion Transformers, directly for 3D shape generation.
  Evaluated on the ABO dataset, our generated shapes with patch structures
  achieve point cloud FID comparable to recent 3D generative models, while
  naturally supporting PBR material generation.
paper_ai_summary: Object Images convert 3D models into a manageable 2D format using image generation models like Diffusion Transformers, providing realistic 3D shapes and PBR materials.
paper_ai_keywords: Object Images 3D models UV maps surface geometry appearance patch structures polygonal meshes Diffusion Transformers point cloud FID PBR material generation