SceneX: Procedural Controllable Large-Scale Scene Generation
Mengqi Zhou, Yuxi Wang, Jun Hou, Shougao Zhang, Yiwei Li, Chuanchen Luo, Junran Peng, Zhaoxiang Zhang
Abstract
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.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 0c088621-df1f-4fae-9f4e-019f571552f2Cited by top-tier papers4
- WorldGen: From Text to Traversable and Interactive 3D WorldsDilin Wang, Hyunyoung Jung, Tom Monnier, Kihyuk Sohn et al.CVPR 2026 · 24 citations
- Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic ExpansionKeyang Lu, Sifan Zhou, Hongbin Xu, Gang Xu et al.CVPR 2026 · 9 citations
- ShapeCraft: LLM Agents for Structured, Textured and Interactive 3D ModelingShuyuan Zhang, Chenhan Jiang, Zuoou Li, Jiankang DengNeurIPS 2025 · 6 citations
- End-to-End Hyper-Relational Information Extraction for Engineering Diagrams via Dynamically Tokenized Relation TransformerTianyou Bai, Yan-Ming Zhang, Zixiang Zhang, Jibin Zhou et al.CVPR 2026
Builds on21
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsJason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma et al.NeurIPS 2022 · 22,562 citations
- HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging FaceYongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li et al.NeurIPS 2023 · 1,778 citations
- Zero-1-to-3: Zero-shot One Image to 3D ObjectRuoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov et al.ICCV 2023 · 1,662 citations
Related papers
- SceneGenesis: 3D Scene Synthesis via Semantic Structural Priors and Mesh-Guided Video-Geometry FusionYueming Zhao, Hongyu Yang, Di HuangAAAI 2026
- Controllable Procedural Generation of LandscapesJia-Hong Liu, Shao-Kui Zhang, Chuyue Zhang, Song-Hai ZhangACM MM 2024 · 6 citations
- SceneCraft: An LLM Agent for Synthesizing 3D Scenes as Blender CodeZiniu Hu, Ahmet Iscen, Aashi Jain, Thomas Kipf et al.ICML 2024 · 105 citations
- Towards Text-guided 3D Scene CompositionQihang Zhang, Chaoyang Wang, Aliaksandr Siarohin, Peiye Zhuang et al.CVPR 2024 · 16 citations
- FilmSceneDesigner: Chaining Set Design for Procedural Film Scene GenerationZhifeng Xie, Keyi Zhang, Yiye Yan, Yuling Guo et al.AAAI 2026
