title: Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object
    Structure via HyperNetworks
summary: Solving image-to-3D from a single view is an ill-posed problem, and current
  neural reconstruction methods addressing it through diffusion models still rely
  on scene-specific optimization, constraining their generalization capability.
  To overcome the limitations of existing approaches regarding generalization and
  consistency, we introduce a novel neural rendering technique. Our approach
  employs the signed distance function as the surface representation and
  incorporates generalizable priors through geometry-encoding volumes and
  HyperNetworks. Specifically, our method builds neural encoding volumes from
  generated multi-view inputs. We adjust the weights of the SDF network
  conditioned on an input image at test-time to allow model adaptation to novel
  scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts
  derived from the synthesized views, we propose the use of a volume transformer
  module to improve the aggregation of image features instead of processing each
  viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we
  avoid the bottleneck of scene-specific optimization and maintain consistency
  across the images generated from multiple viewpoints. Our experiments show the
  advantages of our proposed approach with consistent results and rapid
  generation.
paper_authors._id: 658e58bdf0152a21fc99eaa1
paper_authors.name: Christian Simon
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T14:26:54.349Z
paper_authors._id: 63394c4b20c058d8e235a133
paper_authors.avatarUrl: /avatars/c6203765918ff588bfcf857d1d5adecc.svg
paper_authors.fullname: christian simon
paper_authors.type: user
paper_authors.user: cssen
paper_authors._id: 658e58bdf0152a21fc99eaa2
paper_authors.name: Sen He
paper_authors._id: 658e58bdf0152a21fc99eaa3
paper_authors.name: Juan-Manuel Perez-Rua
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-03T14:45:47.967Z
paper_authors._id: 6572f4d86a1344d777fa7535
paper_authors.avatarUrl: /avatars/eb19a0d4e8d8019456a78a7d621d0955.svg
paper_authors.fullname: Juanma Perez
paper_authors.type: user
paper_authors.user: jperezrua
paper_authors._id: 658e58bdf0152a21fc99eaa4
paper_authors.name: Frost Xu
paper_authors._id: 658e58bdf0152a21fc99eaa5
paper_authors.name: Amine Benhalloum
paper_authors._id: 658e58bdf0152a21fc99eaa6
paper_authors.name: Tao Xiang
paper_title: Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object
    Structure via HyperNetworks
paper_summary: Solving image-to-3D from a single view is an ill-posed problem, and current
  neural reconstruction methods addressing it through diffusion models still rely
  on scene-specific optimization, constraining their generalization capability.
  To overcome the limitations of existing approaches regarding generalization and
  consistency, we introduce a novel neural rendering technique. Our approach
  employs the signed distance function as the surface representation and
  incorporates generalizable priors through geometry-encoding volumes and
  HyperNetworks. Specifically, our method builds neural encoding volumes from
  generated multi-view inputs. We adjust the weights of the SDF network
  conditioned on an input image at test-time to allow model adaptation to novel
  scenes in a feed-forward manner via HyperNetworks. To mitigate artifacts
  derived from the synthesized views, we propose the use of a volume transformer
  module to improve the aggregation of image features instead of processing each
  viewpoint separately. Through our proposed method, dubbed as Hyper-VolTran, we
  avoid the bottleneck of scene-specific optimization and maintain consistency
  across the images generated from multiple viewpoints. Our experiments show the
  advantages of our proposed approach with consistent results and rapid
  generation.
paper_ai_summary: A neural rendering technique using signed distance functions, geometry-encoding volumes, and HyperNetworks achieves scene generalization and consistency without scene-specific optimization.
paper_ai_keywords: signed distance function geometry-encoding volumes HyperNetworks volume transformer Hyper-VolTran scene generalization consistency