WorldGrow: Generating Infinite 3D World

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.

Authors

• Sikuang Li MoE Key Lab of Artificial Intelligence, School of Computer Science, Shanghai Jiao Tong University
• Chen Yang Shanghai Jiao Tong University
• Jiemin Fang Huawei Technologies Co., Ltd.
• Taoran Yi School of EIC, Huazhong University of Science and Technology, China
• Jia Lu Huazhong University of Science and Technology
• Jiazhong Cen Shanghai Jiao Tong University
• Lingxi Xie Huawei Technologies Ltd.
• Wei Shen
• Qi Tian Huawei Inc.

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