Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 9 references
Abstract
In emergency scenarios such as fires or earthquakes, rapid and accurate situational awareness of the scene is critical for decision-making processes. Operators may inaccurately visualize descriptions received from victims under stress. In this study, a web-based system is proposed that instantly converts natural language environment descriptions into three-dimensional (3D) scene visualizations. Although Generative AI approaches produce photorealistic images, they carry the risk of hallucination. Therefore, this study adopts a deterministic Scene Assembly approach that prioritizes spatial consistency. The system converts user text into a structured JSON format via an LLM-based parser, validates physical consistency through a rulebased spatial inference layer, and computes evacuation routes using the A* algorithm. Experimental results on 100 scenarios show that while LLM-only achieves 76.3% overall accuracy, the addition of the spatial constraint layer raises this to 85.7%.
In the context of urban planning, architects are normally instructed with creating presentation images that visualize proposed buildings within their urban context. This work aims to develop a GenAI model for automatically generating architectural presentation images in urban scenes, with emphasis on model optimization. To achieve this, we developed Mask-based Weighted Conditional Flow Matching (MWCFM), which extends Flow Matching by introducing contextual masks for precise feature focusing. This enables targeted training on critical spatial elements relevant to urban planning. Our trained model learns from urban street-view data while adhering to specific style-guidelines, which are integrated into training through the loss function. Furthermore, the model's performance is evaluated using application related metrics, derived from presentation image style guidelines.
Katharina Roth, Eva Hagen, Alexander Bartscher et al.· 0 citations
TerraLogic, a benchmark for geospatial reasoning, is introduced and HieraPlan, a tool-augmented agent that organizes toolkits into functional hierarchies and performs fault-tolerant reasoning is proposed, providing a strong baseline with improved reasoning, cross-modal generalization, and error handling.
Yuhang Yan, Linchao Mou, Bokang Yang et al.· 1 citation
The Cinematic Pre-Visualization System is an end-to-end pipeline that converts natural language scene descriptions into structured three-dimensional (3D) previsualization scenes in Blender and Unity. The system employs Natural Language Processing (NLP) techniques using the spaCy library to extract semantic scene elements including characters, objects, spatial relationships, lighting conditions, and camera directives from textual input. Extracted information is serialized into a platform-agnostic JSON intermediate representation, which drives automated 3D scene construction in Blender via the bpy Python API and in Unity via C# scripting. Experimental evaluation on twenty diverse cinematic scene descriptions demonstrates an average entity extraction accuracy of 87.5%, spatial relationship accuracy of 82.3%, lighting keyword detection of 94.1%, and camera directive extraction of 90.0%. The total pipeline latency from text input to rendered scene is under 6 seconds, validating the system's suitability for iterative use in real-world pre-production workflows. The system significantly reduces scene setup time and lowers the technical barrier for previsualization, offering a practical tool for the film, animation, and game production industries.
B. Dheepa, Monish J, Praveen R et al.· Advanced International Journ...· 0 citations
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.
Hongkun Wang, Fei Li· Digital Signal and Computer...· 0 citations
Lang2Graph is presented, an experimental framework for indoor topological graph inference from natural-language navigational instructions that isolates four governing factors: instruction structure, metadata clarity, prompting strategy, and model size and reasoning capability, and Reasoning-aligned open-source models of moderate scale (14B parameters) outperform larger proprietary models on the most challenging instruction categories.
Moamin Ibrahim, Yaqoob Ansari, Khaled A. Harras et al.· International Conference on...· 0 citations
This paper proposes UrbanGround, the first sandbox to make this question testable in a physically constrained replica of Hong Kong built from territory-wide 3D geospatial data, and hopes it will support broader study of how far current MLLM agents can explore reliably in complex, open-ended urban environments.
Tianjie Ju, Zheng Wu, Yueqing Sun et al.· 0 citations