RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale
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 Reality-Aligned Intelligent Synthesis Engine that creates detailed, City-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.
69283863805584b28040507a
Shengyuan Wang
69283863805584b28040507b
Zhiheng Zheng
69283863805584b28040507c
Yu Shang
69283863805584b28040507d
Lixuan He
69283863805584b28040507e
Yangcheng Yu
69283863805584b28040507f
Fan Hangyu
69283863805584b280405080
Jie Feng
claimed_verified
2025-11-27T20:39:44.514Z
6465d3bd63e7e09dd02e95c3
/avatars/b2798bd5f8368f956bf7fab79d9432f0.svg
Jie Feng
JJ-TMT
user
JJ-TMT
69283863805584b280405081
Qingmin Liao
69283863805584b280405082
Yong Li
RAISECity: A Multimodal Agent Framework for Reality-Aligned 3D World Generation at City-Scale
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 Reality-Aligned Intelligent Synthesis Engine that creates detailed, City-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.
RAISECity generates high-quality, city-scale 3D worlds with real-world alignment, using an agentic framework with multimodal tools, iterative refinement, and advanced representations.
agentic framework multimodal foundation tools dynamic data processing iterative self-reflection reality-aligned shape precision texture fidelity aesthetics level perceptual quality computer graphics pipelines