  We introduce the task of mixed-view panorama synthesis, where the goal is to
synthesize a novel panorama given a small set of input panoramas and a
satellite image of the area. This contrasts with previous work which only uses
input panoramas (same-view synthesis), or an input satellite image (cross-view
synthesis). We argue that the mixed-view setting is the most natural to support
panorama synthesis for arbitrary locations worldwide. A critical challenge is
that the spatial coverage of panoramas is uneven, with few panoramas available
in many regions of the world. We introduce an approach that utilizes
diffusion-based modeling and an attention-based architecture for extracting
information from all available input imagery. Experimental results demonstrate
the effectiveness of our proposed method. In particular, our model can handle
scenarios when the available panoramas are sparse or far from the location of
the panorama we are attempting to synthesize.
