title: GPS as a Control Signal for Image Generation
summary: We show that the GPS tags contained in photo metadata provide a useful
  control signal for image generation. We train GPS-to-image models and use them
  for tasks that require a fine-grained understanding of how images vary within a
  city. In particular, we train a diffusion model to generate images conditioned
  on both GPS and text. The learned model generates images that capture the
  distinctive appearance of different neighborhoods, parks, and landmarks. We
  also extract 3D models from 2D GPS-to-image models through score distillation
  sampling, using GPS conditioning to constrain the appearance of the
  reconstruction from each viewpoint. Our evaluations suggest that our
  GPS-conditioned models successfully learn to generate images that vary based on
  location, and that GPS conditioning improves estimated 3D structure.
paper_authors._id: 67906d622ae55818ddfd0d93
paper_authors.name: Chao Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-22T10:08:21.429Z
paper_authors._id: 645ab0b7c266796265baefa4
paper_authors.avatarUrl: /avatars/bdac661996b63c4b2a56881707afa01f.svg
paper_authors.fullname: Chao Feng
paper_authors.type: user
paper_authors.user: chfeng
paper_authors._id: 67906d622ae55818ddfd0d94
paper_authors.name: Ziyang Chen
paper_authors._id: 67906d622ae55818ddfd0d95
paper_authors.name: Aleksander Holynski
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-22T20:49:16.653Z
paper_authors._id: 62f6e2792e53c2efd33faa92
paper_authors.avatarUrl: /avatars/85d94b4022577747b8d2d10a82c2f3c7.svg
paper_authors.fullname: Aleksander Holynski
paper_authors.type: user
paper_authors.user: holynski
paper_authors._id: 67906d622ae55818ddfd0d96
paper_authors.name: Alexei A. Efros
paper_authors._id: 67906d622ae55818ddfd0d97
paper_authors.name: Andrew Owens
paper_title: GPS as a Control Signal for Image Generation
paper_summary: We show that the GPS tags contained in photo metadata provide a useful
  control signal for image generation. We train GPS-to-image models and use them
  for tasks that require a fine-grained understanding of how images vary within a
  city. In particular, we train a diffusion model to generate images conditioned
  on both GPS and text. The learned model generates images that capture the
  distinctive appearance of different neighborhoods, parks, and landmarks. We
  also extract 3D models from 2D GPS-to-image models through score distillation
  sampling, using GPS conditioning to constrain the appearance of the
  reconstruction from each viewpoint. Our evaluations suggest that our
  GPS-conditioned models successfully learn to generate images that vary based on
  location, and that GPS conditioning improves estimated 3D structure.
paper_ai_summary: GPS conditioning enhances image generation and 3D reconstruction by capturing location-specific details.
paper_ai_keywords: GPS tags image generation diffusion model text conditioning score distillation sampling 3D models 2D GPS-to-image models