RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Shengyuan Zheng, Zhiheng Shang, Yu He, Lixuan Yu, Yangcheng Hangyu, Fan Feng, Jie Liao, Qingmin Li, Yong |
| author_facet | Wang, Shengyuan Zheng, Zhiheng Shang, Yu He, Lixuan Yu, Yangcheng Hangyu, Fan Feng, Jie Liao, Qingmin Li, Yong |
| contents | City-scale 3D generation is of great importance for the development of embodied intelligence and world models. Existing methods, however, face significant challenges regarding quality, fidelity, and scalability in 3D world generation. Thus, we propose RAISECity, a \textbf{R}eality-\textbf{A}ligned \textbf{I}ntelligent \textbf{S}ynthesis \textbf{E}ngine that creates detailed, \textbf{C}ity-scale 3D worlds. We introduce an agentic framework that leverages diverse multimodal foundation tools to acquire real-world knowledge, maintain robust intermediate representations, and construct complex 3D scenes. This agentic design, featuring dynamic data processing, iterative self-reflection and refinement, and the invocation of advanced multimodal tools, minimizes cumulative errors and enhances overall performance. Extensive quantitative experiments and qualitative analyses validate the superior performance of RAISECity in real-world alignment, shape precision, texture fidelity, and aesthetics level, achieving over a 90% win-rate against existing baselines for overall perceptual quality. This combination of 3D quality, reality alignment, scalability, and seamless compatibility with computer graphics pipelines makes RAISECity a promising foundation for applications in immersive media, embodied intelligence, and world models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale Wang, Shengyuan Zheng, Zhiheng Shang, Yu He, Lixuan Yu, Yangcheng Hangyu, Fan Feng, Jie Liao, Qingmin Li, Yong Computer Vision and Pattern Recognition City-scale 3D generation is of great importance for the development of embodied intelligence and world models. Existing methods, however, face significant challenges regarding quality, fidelity, and scalability in 3D world generation. Thus, we propose RAISECity, a \textbf{R}eality-\textbf{A}ligned \textbf{I}ntelligent \textbf{S}ynthesis \textbf{E}ngine that creates detailed, \textbf{C}ity-scale 3D worlds. We introduce an agentic framework that leverages diverse multimodal foundation tools to acquire real-world knowledge, maintain robust intermediate representations, and construct complex 3D scenes. This agentic design, featuring dynamic data processing, iterative self-reflection and refinement, and the invocation of advanced multimodal tools, minimizes cumulative errors and enhances overall performance. Extensive quantitative experiments and qualitative analyses validate the superior performance of RAISECity in real-world alignment, shape precision, texture fidelity, and aesthetics level, achieving over a 90% win-rate against existing baselines for overall perceptual quality. This combination of 3D quality, reality alignment, scalability, and seamless compatibility with computer graphics pipelines makes RAISECity a promising foundation for applications in immersive media, embodied intelligence, and world models. |
| title | RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.18005 |