Urban Scene Generation

Urban scene generation focuses on computationally creating realistic 3D city environments, primarily for applications like autonomous driving simulation and urban planning. Current research emphasizes developing models that produce diverse and controllable scenes, often leveraging techniques like diffusion models, generative adversarial networks (GANs), and large language models (LLMs) to achieve high-quality outputs and incorporate semantic information from various sources, including satellite imagery and textual descriptions. These advancements are significant because they provide valuable synthetic data for training and testing AI systems, improving infrastructure design, and facilitating more realistic simulations for various applications.