title: Make-A-Shape: a Ten-Million-scale 3D Shape Model
summary: Significant progress has been made in training large generative models for
  natural language and images. Yet, the advancement of 3D generative models is
  hindered by their substantial resource demands for training, along with
  inefficient, non-compact, and less expressive representations. This paper
  introduces Make-A-Shape, a new 3D generative model designed for efficient
  training on a vast scale, capable of utilizing 10 millions publicly-available
  shapes. Technical-wise, we first innovate a wavelet-tree representation to
  compactly encode shapes by formulating the subband coefficient filtering scheme
  to efficiently exploit coefficient relations. We then make the representation
  generatable by a diffusion model by devising the subband coefficients packing
  scheme to layout the representation in a low-resolution grid. Further, we
  derive the subband adaptive training strategy to train our model to effectively
  learn to generate coarse and detail wavelet coefficients. Last, we extend our
  framework to be controlled by additional input conditions to enable it to
  generate shapes from assorted modalities, e.g., single/multi-view images, point
  clouds, and low-resolution voxels. In our extensive set of experiments, we
  demonstrate various applications, such as unconditional generation, shape
  completion, and conditional generation on a wide range of modalities. Our
  approach not only surpasses the state of the art in delivering high-quality
  results but also efficiently generates shapes within a few seconds, often
  achieving this in just 2 seconds for most conditions.
paper_authors._id: 65af502248481b399a472331
paper_authors.name: Ka-Hei Hui
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T10:42:50.483Z
paper_authors._id: 64d67c5aaa89fb548aa3a391
paper_authors.avatarUrl: /avatars/971455c8465d4828aee2379f87c4b874.svg
paper_authors.fullname: Ka-Hei Hui
paper_authors.type: user
paper_authors.user: khhui
paper_authors._id: 65af502248481b399a472332
paper_authors.name: Aditya Sanghi
paper_authors._id: 65af502248481b399a472333
paper_authors.name: Arianna Rampini
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-23T10:43:18.119Z
paper_authors._id: 653a83318f8d60f2046b27b1
paper_authors.avatarUrl: /avatars/ab90522fa53738a6f67aca189ec45e6f.svg
paper_authors.fullname: Arianna Rampini
paper_authors.type: user
paper_authors.user: rampina
paper_authors._id: 65af502248481b399a472334
paper_authors.name: Kamal Rahimi Malekshan
paper_authors._id: 65af502248481b399a472335
paper_authors.name: Zhengzhe Liu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-01-23T11:00:38.808Z
paper_authors._id: 633e1588e7f8b20a2dce5b44
paper_authors.avatarUrl: /avatars/8ce0db03b19c3970c0862c490485e831.svg
paper_authors.fullname: zzliu
paper_authors.type: user
paper_authors.user: xiaoxinge
paper_authors._id: 65af502248481b399a472336
paper_authors.name: Hooman Shayani
paper_authors._id: 65af502248481b399a472337
paper_authors.name: Chi-Wing Fu
paper_title: Make-A-Shape: a Ten-Million-scale 3D Shape Model
paper_summary: Significant progress has been made in training large generative models for
  natural language and images. Yet, the advancement of 3D generative models is
  hindered by their substantial resource demands for training, along with
  inefficient, non-compact, and less expressive representations. This paper
  introduces Make-A-Shape, a new 3D generative model designed for efficient
  training on a vast scale, capable of utilizing 10 millions publicly-available
  shapes. Technical-wise, we first innovate a wavelet-tree representation to
  compactly encode shapes by formulating the subband coefficient filtering scheme
  to efficiently exploit coefficient relations. We then make the representation
  generatable by a diffusion model by devising the subband coefficients packing
  scheme to layout the representation in a low-resolution grid. Further, we
  derive the subband adaptive training strategy to train our model to effectively
  learn to generate coarse and detail wavelet coefficients. Last, we extend our
  framework to be controlled by additional input conditions to enable it to
  generate shapes from assorted modalities, e.g., single/multi-view images, point
  clouds, and low-resolution voxels. In our extensive set of experiments, we
  demonstrate various applications, such as unconditional generation, shape
  completion, and conditional generation on a wide range of modalities. Our
  approach not only surpasses the state of the art in delivering high-quality
  results but also efficiently generates shapes within a few seconds, often
  achieving this in just 2 seconds for most conditions.
paper_ai_summary: Make-A-Shape, a novel 3D generative model, efficiently trains using wavelet-tree representation and diffusion model techniques, generating high-quality shapes from various inputs in under two seconds.
paper_ai_keywords: wavelet-tree representation diffusion model subband coefficient filtering subband coefficients packing subband adaptive training strategy unconditional generation shape completion conditional generation single/multi-view images point clouds low-resolution voxels