CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View
  Synthesis
Authors: name: Weijia Li, name: Jun He, name: Junyan Ye, name: Huaping Zhong, name: Zhimeng Zheng, name: Zilong Huang, name: Dahua Lin, name: Conghui He

Satellite-to-street view synthesis aims at generating a realistic street-view
image from its corresponding satellite-view image. Although stable diffusion
models have exhibit remarkable performance in a variety of image generation
applications, their reliance on similar-view inputs to control the generated
structure or texture restricts their application to the challenging cross-view
synthesis task. In this work, we propose CrossViewDiff, a cross-view diffusion
model for satellite-to-street view synthesis. To address the challenges posed
by the large discrepancy across views, we design the satellite scene structure
estimation and cross-view texture mapping modules to construct the structural
and textural controls for street-view image synthesis. We further design a
cross-view control guided denoising process that incorporates the above
controls via an enhanced cross-view attention module. To achieve a more
comprehensive evaluation of the synthesis results, we additionally design a
GPT-based scoring method as a supplement to standard evaluation metrics. We
also explore the effect of different data sources (e.g., text, maps, building
heights, and multi-temporal satellite imagery) on this task. Results on three
public cross-view datasets show that CrossViewDiff outperforms current
state-of-the-art on both standard and GPT-based evaluation metrics, generating
high-quality street-view panoramas with more realistic structures and textures
across rural, suburban, and urban scenes. The code and models of this work will
be released at https://opendatalab.github.io/CrossViewDiff/.