WorldSculpt: Generating Compositional Worlds from Grounded Videos

Generating large-scale cluttered scenes as individually editable object meshes is challenging. Existing compositional methods largely target simple scenes. Our key insight is that, with appropriate design, a single-object generative prior can scale to large-scale, densely cluttered scenes without scene-level training. We introduce $\text{WorldSculpt}$, which realizes this idea through canonical-space multi-view conditioning, training-time augmentation, and object-wise scene composition. Finetuned entirely on single-object data, it generates hundreds of mutually occluding objects as complete, spatially aligned meshes in a shared world frame. We further introduce a photorealistic benchmark of densely cluttered scenes with per-object annotations. Evaluations demonstrate superior geometric accuracy and observation alignment over prior approaches. We also demonstrate broader applicability by converting 3DGS worlds from Marble into compositional scenes.