  We present a novel method, Aerial Diffusion, for generating aerial views from
a single ground-view image using text guidance. Aerial Diffusion leverages a
pretrained text-image diffusion model for prior knowledge. We address two main
challenges corresponding to domain gap between the ground-view and the aerial
view and the two views being far apart in the text-image embedding manifold.
Our approach uses a homography inspired by inverse perspective mapping prior to
finetuning the pretrained diffusion model. Additionally, using the text
corresponding to the ground-view to finetune the model helps us capture the
details in the ground-view image at a relatively low bias towards the
ground-view image. Aerial Diffusion uses an alternating sampling strategy to
compute the optimal solution on complex high-dimensional manifold and generate
a high-fidelity (w.r.t. ground view) aerial image. We demonstrate the quality
and versatility of Aerial Diffusion on a plethora of images from various
domains including nature, human actions, indoor scenes, etc. We qualitatively
prove the effectiveness of our method with extensive ablations and comparisons.
To the best of our knowledge, Aerial Diffusion is the first approach that
performs ground-to-aerial translation in an unsupervised manner.
