WorldExpand: Building Coherent 3D Worlds via Sparse Joint Panoramic Expansion

Constructing complete 3D worlds from a single panorama is challenging because large portions of the scene remain unobserved. Existing panoramic-video methods expand spatial coverage through dense sequential generation, but neighboring frames contain substantial redundancy, long trajectories accumulate errors, and independently generated directions may be inconsistent. We present WorldExpand, which formulates single-panorama world expansion as the generation of sparse, complementary, high-resolution panoramic observations. This representation concentrates the generation budget on informative scene regions and preserves the fine-grained details required for 3D construction. WorldExpand addresses two key challenges: where to observe and how to synthesize. Our Coverage-Guided View Selection identifies informative target locations using scene-adaptive quadrants and warp-based coverage scores, while our Joint Panoramic Diffusion uses Gaussian-splatting-based geometric warps to jointly synthesize four pose-grounded, cross-view-consistent panoramas. A diagnostic-guided global–local attention schedule retains global attention only in sensitive layers, reducing the training and inference costs of high-resolution joint generation. The resulting posed panoramas can be directly fed into an existing reconstruction pipeline to build an explicit 3D representation. Experiments show that WorldExpand recovers content behind occluders and around distant corners, improves both novel-view synthesis and downstream 3D reconstruction metrics across various datasets, and generalizes robustly to diverse in-the-wild scenes. Compared with the state-of-the-art panoramic-video method, WorldExpand achieves up to an $8.6\times$ speed-up with higher-resolution observations and stronger cross-view consistency.