From understanding to rendering: multimodal LLM-guided green retrofitting of urban street views
Urban street retrofitting is increasingly used to improve greenery, walkability, and perceived safety, yet planners and communities often lack intuitive visualizations of how a street may look after such interventions. This paper presents a perception-aware framework for green streetscape redesign that transforms a real street-view image and a high-level design goal into a realistic visualization of a retrofitted street. The proposed framework integrates an MLLM-guided planner for structured redesign operations, a rule-based compiler for semantic mask editing, a ControlNet-guided diffusion renderer for candidate generation, and a perception-aware selector for choosing the final design. Experiments on Cityscapes and Mapillary Vistas, show that the method achieves a favorable balance among environmental improvement, image realism, and structural preservation. Additional comparisons further demonstrate the value of multimodal planning and perception-aware ranking. These results suggest that controllable generative models can provide practical support for urban street retrofit visualization and assessment.