The rapid development of Artificial Intelligence (AI) in fashion design has enhanced the efficiency of clothing production and customization. However, conventional design systems often fail to effectively combine text and image information, such as sketches and product details, when creating innovative and user-centric designs. This paper proposes an Intelligent Clothing Design Interaction System (ICDIS) based on multimodal learning to improve AI-aided fashion design. The ICDIS system combines computer vision and Natural Language Processing (NLP) synergistically to provide an optimized, more efficient fashion design process. The proposed ICDIS utilizes multimodal learning by integrating transformer-based NLP for text description and computer vision for image processing. With the integration, real-time design modification is enabled, allowing designers to iteratively enhance outputs based on text input and visual feedback, thereby improving the AI-aided fashion design process to be more interactive, efficient, and creative. Empirical tests demonstrate that ICDIS enhances design accuracy by 27% (in terms of adherence to professional designer critiques) and accelerates design iterations by 35% compared to conventional AI-based design environments. Users’ satisfaction and engagement rates also rose by 22%, underscoring the efficacy of multimodal interaction for creative tasks. This work has extensive potential for fashion design, e-shopping, and online personalization, where AI-based programs can increase creativity and efficiency. In its ability to bridge the gap between text- based inspiration and image-based design, ICDIS represents a groundbreaking step toward a more informed, interactive apparel design paradigm.

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