title: Hash3D: Training-free Acceleration for 3D Generation
summary: The evolution of 3D generative modeling has been notably propelled by the
  adoption of 2D diffusion models. Despite this progress, the cumbersome
  optimization process per se presents a critical hurdle to efficiency. In this
  paper, we introduce Hash3D, a universal acceleration for 3D generation without
  model training. Central to Hash3D is the insight that feature-map redundancy is
  prevalent in images rendered from camera positions and diffusion time-steps in
  close proximity. By effectively hashing and reusing these feature maps across
  neighboring timesteps and camera angles, Hash3D substantially prevents
  redundant calculations, thus accelerating the diffusion model's inference in 3D
  generation tasks. We achieve this through an adaptive grid-based hashing.
  Surprisingly, this feature-sharing mechanism not only speed up the generation
  but also enhances the smoothness and view consistency of the synthesized 3D
  objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models,
  demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency
  by 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian
  splatting largely speeds up 3D model creation, reducing text-to-3D processing
  to about 10 minutes and image-to-3D conversion to roughly 30 seconds. The
  project page is at https://adamdad.github.io/hash3D/.
paper_authors._id: 6615efc5c7657f570b74b41c
paper_authors.name: Xingyi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-10T10:58:09.176Z
paper_authors._id: 634cfebc350bcee9bed20a4d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634cfebc350bcee9bed20a4d/fN47nN5rhw-HJaFLBZWQy.png
paper_authors.fullname: Xingyi Yang
paper_authors.type: user
paper_authors.user: adamdad
paper_authors._id: 6615efc5c7657f570b74b41d
paper_authors.name: Xinchao Wang
paper_title: Hash3D: Training-free Acceleration for 3D Generation
paper_summary: The evolution of 3D generative modeling has been notably propelled by the
  adoption of 2D diffusion models. Despite this progress, the cumbersome
  optimization process per se presents a critical hurdle to efficiency. In this
  paper, we introduce Hash3D, a universal acceleration for 3D generation without
  model training. Central to Hash3D is the insight that feature-map redundancy is
  prevalent in images rendered from camera positions and diffusion time-steps in
  close proximity. By effectively hashing and reusing these feature maps across
  neighboring timesteps and camera angles, Hash3D substantially prevents
  redundant calculations, thus accelerating the diffusion model's inference in 3D
  generation tasks. We achieve this through an adaptive grid-based hashing.
  Surprisingly, this feature-sharing mechanism not only speed up the generation
  but also enhances the smoothness and view consistency of the synthesized 3D
  objects. Our experiments covering 5 text-to-3D and 3 image-to-3D models,
  demonstrate Hash3D's versatility to speed up optimization, enhancing efficiency
  by 1.3 to 4 times. Additionally, Hash3D's integration with 3D Gaussian
  splatting largely speeds up 3D model creation, reducing text-to-3D processing
  to about 10 minutes and image-to-3D conversion to roughly 30 seconds. The
  project page is at https://adamdad.github.io/hash3D/.
paper_ai_summary: Hash3D accelerates 3D generation by hashing and reusing feature maps, enhancing smoothness and reducing processing time for text-to-3D and image-to-3D models.
paper_ai_keywords: 3D generative modeling 2D diffusion models feature-map redundancy adaptive grid-based hashing 3D Gaussian splatting text-to-3D image-to-3D