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SceneX: Procedural Controllable Large-scale Scene Generation

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arxiv 2403.15698 v3 pith:MFDOULCK submitted 2024-03-23 cs.CV cs.AI

classification cs.CVcs.AI
keywords generationscenecontrollablelarge-scaleproceduralassetschallengesextensive
verification ladder T0 review T1 audit T2 compute T3 formal
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Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG faces challenges such as limited modular diversity, high expertise requirements, and challenges in managing the diverse elements and structures in complex scenes. In this paper, we introduce a large-scale scene generation framework, SceneX, which can automatically produce high-quality procedural models according to designers' textual descriptions. Specifically, the proposed method comprises two components, PCGHub and PCGPlanner. The former encompasses an extensive collection of accessible procedural assets and thousands of hand-craft API documents to perform as a standard protocol for PCG controller. The latter aims to generate executable actions for Blender to produce controllable and precise 3D assets guided by the user's instructions. Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Sat2City generates explicit 3D city geometry and appearance from a height-map condition using cascaded latent diffusion on sparse voxel grids, beating prior methods on a new synthetic city dataset.

  2. LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation

    cs.AI 2025-09 reject novelty 5.0 of 10

    A multimodal LLM framework generates interactive Unreal-based 3D environments from text and height maps, claiming superior layout accuracy and over 90x faster production than manual methods.

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