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.
68fecebf6cdff8b857f470e5
Sikuang Li
68fecebf6cdff8b857f470e6
Chen Yang
68fecebf6cdff8b857f470e7
Jiemin Fang
claimed_verified
2025-10-27T10:27:40.128Z
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Jiemin Fang
JieminFang
user
JieminFang
68fecebf6cdff8b857f470e8
Taoran Yi
claimed_verified
2025-10-29T12:50:27.836Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64e5ee4b93d04e3439f4e988/g-THJvunoSPUyQ2v_fChU.jpeg
taoranyi
thewhole
user
thewhole
68fecebf6cdff8b857f470e9
Jia Lu
68fecebf6cdff8b857f470ea
Jiazhong Cen
68fecebf6cdff8b857f470eb
Lingxi Xie
68fecebf6cdff8b857f470ec
Wei Shen
68fecebf6cdff8b857f470ed
Qi Tian
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.
WorldGrow, a hierarchical framework, generates large, continuous 3D environments with coherent geometry and realistic appearance using pre-trained 3D models and a coarse-to-fine generation strategy.
3D implicit representations 3D foundation models scene block generation data curation pipeline 3D block inpainting coarse-to-fine generation geometry reconstruction 3D-FRONT dataset world models