Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion

2025-11-23

AI Summary

Existing 3D urban generation methods rely on monolithic diffusion models, limiting both personalization and scalable expansion. To address this, we propose a top-down hierarchical planning framework—“City–District–Grid”—that integrates large language model (LLM)-driven reasoning to enable user-guided, customizable design and continuous urban evolution. Our key innovation is a relation-guided interactive expansion mechanism, incorporating scene-graph-aware distance constraints and semantic layout optimization to ensure spatial coherence. We further introduce a multi-dimensional evaluation benchmark covering semantic fidelity, geometric accuracy, texture quality, and layout合理性, with six quantitative metrics. Leveraging a “generate–optimize–evaluate” image synthesis loop and image-to-3D reconstruction, our method jointly synthesizes hierarchical structure and local details. Experiments demonstrate state-of-the-art performance across generation quality, scalability, and user controllability.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualize the city through a top-down planning strategy that defines a hierarchical "City-District-Grid" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a "produce-refine-evaluate" isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.

Problem

• Generating personalized and boundless 3D city scenes using single diffusion models
• Creating user-customized infinite city expansion with coherent spatial relationships
• Evaluating 3D city generation quality across semantics, geometry, and layout

Innovation

• Hierarchical City-District-Grid planning structure
• Produce-refine-evaluate loop for 3D generation
• Scene graph-based expansion for coherent growth