InstaPano: Zero-shot Instance Layout Controlled Panorama Generation Via Global Attention Fusion

Achieving both global semantic coherence and precise in-stance level control in wide-aspect-ratio panorama generation is an un-resolved challenge. Existing methods that synchronize independent viewsto generate panoramas often lack semantic coherence and struggle withfine-grained multi-instance placement, resulting in contextual artifactsand fragmented objects. We introduce InstaPano, a training-free frame-work for zero-shot instance-controlled panorama generation. InstaPanointegrates a Global Attention Fusion mechanism into a pre-trained layout-to-image model. Through Sync-Fuse-Dispatch workflow, it periodicallyaggregates latent features from all local views to construct a unifiedglobal context, performs multi-level attention computation over this con-text to achieve true fusion, and then dispatches the enriched global fea-tures back to each view. This enables the coherent rendering of complexpanoramas with multiple specified instances. Furthermore, we introducea conditional positional mask to resolve object repetition artifacts thatmay arise in large bounding boxes. On a newly constructed yet challeng-ing benchmark for panoramic instance layout control, InstaPano achievessuperior performance in both layout fidelity and semantic coherence,faithfully generating complex panoramic scenes.