Unveil as You Go: Rethinking Spatial Exploration for Single-Image World Generation

The integration of generative models and reconstruction networks has emerged as a promising paradigm for 3D Gaussian world generation. However, existing methods mostly treat camera exploration as a plug-and-play input rather than scene-adaptive understanding, and the spatial priors encoded in pretrained vision–language models remain underexploited. To address this, we propose UnveilWorld, in which the explorer performs recursive planning and understanding and the generator unveils unobserved views, jointly contributing to the final world reconstruction. To further investigate the synergy between spatial understanding and generation, UnveilWorld is post-trained with hard-state step-wise AWM, pairing stage-wise group-relative credit assignment with failure-biased state propagation. The joint optimization of the full pipeline closes the loop among exploration, generation, and reconstruction end-to-end. Experiments demonstrate that UnveilWorld generates high-quality 3D worlds with strong cross-view consistency in 18.12 seconds on a single NVIDIA A800 GPU.