PanoWeaver: Generative Virtual Camera for Panoramic Reframing

Panoramic cameras capture a complete field of view in a single shot, yet converting such omnidirectional observations into well-composed photographs remains challenging. To address this issue, we introduce panorama reframing, a new photography-oriented task that jointly determines where to look and what the resulting perspective view should look like. Then, we propose PanoWeaver, a coupled camera-image generation framework that treats the spherical field of view (SFoV) as a virtual camera action and the perspective image as its visual outcome. Built upon a DiT backbone, PanoWeaver jointly generates both through bidirectional interaction, coupling explicit camera inference with generative visual modeling to learn camera–image correspondence. Moreover, we construct PanoComp-100K, a large-scale panoramic composition dataset containing 100,200 samples with SFoV states, perspective views, and human composition annotations. Extensive experiments demonstrate that PanoWeaver outperforms existing alternatives in panorama reframing, enabling a practical shoot-first and reframe-later paradigm for 360° photography. The source code, trained models, and dataset will be made available to the public.