Bridging the Gap Between Generative Artificial Intelligence and Innovation in Footwear Design

Abstract

This thesis bridges the gap between generative artificial intelligence and innovation in footwear design, an industry that is quite conservative in its design methods despite the constant demand for innovation. Initial applied research showed that while 3D technologies have the potential to enhance efficiency in development, they are still time-consuming, as the 3D generative AI was not advanced enough yet and therefore left out of the scope. As a result, the research focused on applying AI on image generation, particularly using LoRA fine tuning to effectively capture brand identity and consistently produce high-quality results with reference images and ControlNet. To validate these findings, the tools and workflows were applied in a case demonstration to a design brief. Additionally, a user test was done with a designer of Filling Pieces, comparing the outcomes of the AI framework versus traditional processes in a survey. The findings showed that the AI framework significantly accelerates the generation of design concepts and enhances creativity by producing more novel designs. However, the study identified limitations in clarity, completeness, and production feasibility, emphasizing the need for AI-generated designs to be complemented with technical drawings for communication with the factory. The relationship between AI and human designers should thus be complementary to achieve the best results.