AI-Generated Graffiti Simulation for Building Façade and City Fabric

Abstract

Graffiti represents a multi-disciplinary social behavior. It is used to annotate urban landscapes under the assumption that building façades will constantly evolve and acquire modified skins. This study aimed to simulate the interaction between building façades and generative AI-based graffiti using Stable Diffusion® (SD v 1.7.0). The context used for graffiti generation considered the graffiti as the third skin, the remodeled façade as the second skin, and the original façade as the first skin. Graffiti was created based on plain-text descriptions, representative images, renderings of scaled 3D prototype models, and characteristic façades obtained from various seed elaborations. It was then generated from either existing graffiti or the abovementioned context; overlaid upon a campus or city; and judged based on various criteria: style, area, altitude, orientation, distribution, and development. I found that rescaling and reinterpreting the context presented the most creative results: it allowed unexpected interactions between the urban fabric and the dynamics created to be foreseen by elaborating on the context and due to the divergent instrumentation used for the first, second, and third skins. With context awareness or homogeneous aggregation, graphic partitions can thus be merged into new topologically re-arranged polygons that enable a cross-gap creative layout. Almost all façades were found to be applicable. AI generation enhances awareness of the urban fabric and facilitates a review of both the human scale and buildings. AI-based virtual governance can use generative graffiti to facilitate the implementation of preventive measures in an urban context.