  Panorama synthesis endeavors to craft captivating 360-degree visual
landscapes, immersing users in the heart of virtual worlds. Nevertheless,
contemporary panoramic synthesis techniques grapple with the challenge of
semantically guiding the content generation process. Although recent
breakthroughs in visual synthesis have unlocked the potential for semantic
control in 2D flat images, a direct application of these methods to panorama
synthesis yields distorted content. In this study, we unveil an innovative
framework for generating high-resolution panoramas, adeptly addressing the
issues of spherical distortion and edge discontinuity through sophisticated
spherical modeling. Our pioneering approach empowers users with semantic
control, harnessing both image and text inputs, while concurrently streamlining
the generation of high-resolution panoramas using parallel decoding. We
rigorously evaluate our methodology on a diverse array of indoor and outdoor
datasets, establishing its superiority over recent related work, in terms of
both quantitative and qualitative performance metrics. Our research elevates
the controllability, efficiency, and fidelity of panorama synthesis to new
levels.
