WorldGrow: Generating Infinite 3D World
Authors: name: Sikuang Li, name: Chen Yang, name: Jiemin Fang, name: Taoran Yi, name: Jia Lu, name: Jiazhong Cen, name: Lingxi Xie, name: Wei Shen, name: Qi Tian

We tackle the challenge of generating the infinitely extendable 3D world --
large, continuous environments with coherent geometry and realistic appearance.
Existing methods face key challenges: 2D-lifting approaches suffer from
geometric and appearance inconsistencies across views, 3D implicit
representations are hard to scale up, and current 3D foundation models are
mostly object-centric, limiting their applicability to scene-level generation.
Our key insight is leveraging strong generation priors from pre-trained 3D
models for structured scene block generation. To this end, we propose
WorldGrow, a hierarchical framework for unbounded 3D scene synthesis. Our
method features three core components: (1) a data curation pipeline that
extracts high-quality scene blocks for training, making the 3D structured
latent representations suitable for scene generation; (2) a 3D block inpainting
mechanism that enables context-aware scene extension; and (3) a coarse-to-fine
generation strategy that ensures both global layout plausibility and local
geometric/textural fidelity. Evaluated on the large-scale 3D-FRONT dataset,
WorldGrow achieves SOTA performance in geometry reconstruction, while uniquely
supporting infinite scene generation with photorealistic and structurally
consistent outputs. These results highlight its capability for constructing
large-scale virtual environments and potential for building future world
models.