CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View
  Synthesis
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/.
66d5b674b66342762fd54d02
Weijia Li
claimed_verified
2024-09-03T07:48:23.479Z
66d5b56c77a026c3d2086a79
/avatars/45da07fd82fd455955faa05b27a6393f.svg
Weijia Li
user
liweijia
66d5b674b66342762fd54d03
Jun He
claimed_verified
2024-10-16T09:44:49.833Z
670ddb69d6ac6394419d88c5
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/XxnGNaX3FWug4aiZVjg93.png
Jun He
user
JunHe0915
66d5b674b66342762fd54d04
Junyan Ye
claimed_verified
2024-09-03T14:48:59.572Z
66978ee0b8656f6506b4acb2
/avatars/298acb8222e189fce4368985ee5374a1.svg
Junyan Ye
user
Yejy53
66d5b674b66342762fd54d05
Huaping Zhong
66d5b674b66342762fd54d06
Zhimeng Zheng
66d5b674b66342762fd54d07
Zilong Huang
66d5b674b66342762fd54d08
Dahua Lin
66d5b674b66342762fd54d09
Conghui He
CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View
  Synthesis
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/.
CrossViewDiff, a cross-view diffusion model, enhances satellite-to-street view synthesis by incorporating structural and textural controls, achieving superior results across various datasets.
cross-view diffusion model satellite scene structure estimation cross-view texture mapping denoising process cross-view attention module GPT-based scoring method