Fernando Moral

Elena Merino-Gómez, Pedro Reviriego, Fernando Moral

Unfinished buildings are a constant throughout the history of architecture and have given rise to intense debates on the opportuneness of their completion, in addition to offering alibis for theorizing about the compositional possibilities in coherence with the finished parts. The development of Artificial Intelligence (AI) opens new avenues for the proposal of possibilities for the completion of unfinished architectures. Specifically, with the recent appearance of tools such as DALL-E, capable of completing images guided by a textual description, it is possible to count on the help of AI for architectural design tasks. In this article we explore the use of these new AI tools for the completion of unfinished facades of historical temples and analyse the still germinal stadium in the field of architectural graphic composition.

Javier Conde, Miguel González, Gonzalo Martínez et al.

The rapid adoption of generative artificial intelligence (AI) is accelerating content creation and modification. For example, variations of a given content, be it text or images, can be created almost instantly and at a low cost. This will soon lead to the majority of text and images being created directly by AI models or by humans assisted by AI. This poses new risks; for example, AI-generated content may be used to train newer AI models and degrade their performance, or information may be lost in the transformations made by AI which could occur when the same content is processed over and over again by AI tools. An example of AI image modifications is inpainting in which an AI model completes missing fragments of an image. The incorporation of inpainting tools into photo editing programs promotes their adoption and encourages their recursive use to modify images. Inpainting can be applied recursively, starting from an image, removing some parts, applying inpainting to reconstruct the image, revising it, and then starting the inpainting process again on the reconstructed image, etc. This paper presents an empirical evaluation of recursive inpainting when using one of the most widely used image models: Stable Diffusion. The inpainting process is applied by randomly selecting a fragment of the image, reconstructing it, selecting another fragment, and repeating the process a predefined number of iterations. The images used in the experiments are taken from a publicly available art data set and correspond to different styles and historical periods. Additionally, photographs are also evaluated as a reference. The modified images are compared with the original ones by both using quantitative metrics and performing a qualitative analysis. The results show that recursive inpainting in some cases modifies the image so that it still resembles the original one while in others leads to degeneration.