GPS as a Control Signal for Image Generation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 79 of its 109 references that are in the map. First posted on arXiv 21 Jan 2025.
Builds on
79 of 109 references.
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38 more
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50 most cited of 79 All 79 (JSON)
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Citing papers per year: 2025: 1; 2026: 4.
5 papers.
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- 2026Interpretation-Oriented Cloud Removal via Observation-Anchored Residual Flow with Geo-Contextual Alignment2 Jul 2026
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- 2026DiffPlace: Street View Generation via Place-Controllable Diffusion Model Enhancing Place Recognition12 Feb 2026
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