Impact of Artificial Intelligence algorithms on architectural visualization. A Practical review

This article investigates the creative potential and practical use of artificial intelligence in architectural visualization by testing three free platforms: Fabrie, Mnml AI and LookX AI Cloud, on a real project involving the design of a single-story house with a summer kitchen. The study aims to assess the extent to which these AI tools can support or enhance the development and refinement of architectural visualizations, both through the generation of new renders based on conceptual sketches and through the processing of existing visual data. The methodology involves the comparative analysis of the platforms’ outputs using four evaluation criteria: clarity of spatial perception, level of detailing, emotional expressiveness, and the format and quality of the final exported visual data. Based on this framework, the study identifies the respective strengths and limitations of each AI system, highlighting distinctive characteristics in render quality, user interface, and interpretive responsiveness to design prompts. The article also formulates practical guidelines for data preparation and prompt formulation, addressing technical constraints, model sensitivity, and the iterative nature of human-AI interaction. A key contribution of the study lies in its dual methodological approach: rendering experiments were conducted in two modes: generation from a blank sketch and enhancement of an existing render, thereby illuminating how AI tools can be integrated into different phases of the design workflow. The study also reflects on the broader implications of human-AI collaboration in architectural practice, focusing on how authorship, visual language, and creative decision-making are redefined in the context of automated design assistance. The scientific novelty of this research lies in its detailed, real-case-based evaluation of emerging AI visualization platforms in architectural work. Its practical significance is evident in the set of recommendations it provides to architects, designers, and educators exploring AI as a co-creative design partner.