DesignGemini: Achieving Highly Personalized Design Empowered by Generative Models and Human Digital Twin

In contemporary digital landscape, the demand for personalized design is significantly influenced by rising consumer expectations. Enabling active user engagement and catering to individual preferences has become essential for creating personalized experiences, improve customer satisfaction and foster brand loyalty. These factors are fundamental to the success of products and services. However, traditional personalized design methodologies often lack active user active participation and are typically implemented through configurations predefined by experts. In this study, we introduce DesignGemini, a highly personalized design approach that integrates generative models with human digital twin. This methodology facilitates the generation of personalized designs through natural language interactions with generative models, while simultaneously enabling online preference recognition and ergonomic analysis via the establishment of human digital twins. Additionally, we present a case study on the personalized design of vehicle seats to demonstrate the feasibility of the proposed approach. This case study effectively showcases product generation utilizing TAPS3D, a text-to-3D generative model, along with rapid ergonomic analysis employing a vision-based human digital twin modeling technique.