Collaborative Mechanism for Creative Design of Cultural IP Based on Generating Adversarial Networks and CAD Technology

Abstract

Generating Adversarial Networks (GAN) stands as a pivotal subdomain within the broader spectrum of artificial intelligence (AI). Its essence lies in simulating authentic data distributions through extensive learning, ultimately generating fresh datasets that resonate with the originals yet exhibit unique distinctions. The study introduces a collaborative framework for cultural IP innovative design, amalgamating the strengths of Generative Adversarial Networks and computer-aided design (CAD). This synergy aims to strike a harmonious balance between creativity and practicality. The findings reveal that the GAN and CAD-driven IP design approach outperforms traditional methods in terms of efficiency and algorithmic stability, coupled with a notable boost in user satisfaction. This outcome not only underscores the transformative potential of emerging technologies in the design realm but also offers innovative insights and practical guidance for the design industry's evolution. The GAN model's robust generative capabilities inject creativity and originality into the design schemes, while CAD's precision ensures their practical feasibility. In conclusion, this research is poised to propel the continual enhancement and refinement of IP design methodologies, better aligning with the aspirations of designers and users alike.