Research on AIGC-driven Footwear Intelligent Design Methods and Applications

Abstract

ObjectiveThis research is aimed at solving the problems exposed by AIGC technology in the field of footwear design, such as generation homogeneity, low process feasibility, and market demand disconnection. A dedicated AIGC generation model and an intelligent design methodology for footwear design are built, which realize the transformation of the design process from the traditional linear mode to a data-driven, human-machine collaboration closed-loop system, and promote the integration of AIGC technology in design innovation and industrial implementation.MethodsThe study adopts a research path of "theoretical analysis, technology construction, system verification, and method refinement" is adopted. In the early theoretical research stage, the application status and bottlenecks of AIGC in footwear design are systematically sorted out through literature analysis and questionnaire survey, providing the theoretical basis and problem orientation for model construction. In the model construction stage, the label is extracted with the help of code detection algorithm, and the footwear model is constructed by using the diffusion model architecture. In the stage of shoe generation design method, the intelligent design method of footwear is proposed by comparing and studying the traditional linear design process and the collaborative process after AIGC intervention. In the stage of building a digital asset library, core data such as materials, processes, soles, and lasts are integrated to build a structured and correlated asset library. In the construction stage of the intelligent design system, from demand analysis to design positioning, a collaborative group of "planning-AI creator-craftsman" is established to carry out multi-end reviews and realize a closed loop of marketing and data feedback.ResultsThe research has achieved the following three outcomes: First, an intelligent auxiliary design system for shoes is constructed: according to its structural characteristics, a detection code is developed to extract the labels of shoe design elements. A large model of shoe generation is built based on the diffusion model, combined with LoRA to accurately control the design language (such as contour lines, material matching, and color system), providing high-quality solution divergence support for the early design stage. Second, the closed-loop reconstruction of the design process is achieved: the traditional linear process of "design→testing→production" has been upgraded to a three-dimensional intelligent closed-loop of "data insight→real-time verification→and dynamic optimization". The design direction is defined through market data and trend analysis, the scheme is quickly generated by AIGC, and the manufacturability is preliminarily evaluated in combination with the database, effectively improving the design response efficiency and feasibility. Third, a human-machine collaborative design path is established: AIGC is not a substitute for designers, but rather expands the boundaries of creative exploration through rapid generation, multi-scheme comparison and stylistic control. It establishes a collaborative work model of "human creativity guidance + AI efficient transformation", which clarifies the core position of designers in creative planning, aesthetic judgment and cultural narrative, while AI tools exert efficiency advantages in stages such as scheme transformation, component combination, and parametric adjustment.ConclusionsThe research confirms that AIGC technology should move towards a development path with a high degree of human-machine collaboration as the core: technically, developing a generation architecture that integrates footwear specialties by embedding biomechanical parameters, material properties and process constraints to enhance the rationality and feasibility of the model; procedurally, building a traceable and interpretable generation system in the process to achieve transparent association from concept to element and rebuild the authenticity and credibility of the design process; and instituionally, establishing an ethical framework covering copyright identification, contribution assessment, and cultural compliance to promote the formation of a human-machine co-creation environment with clear rights and responsibilities, collaboration and order. Finally, it will promote the development of AIGC from an efficiency tool to a co-creation partner, and build an industrial application system with sustainable evolution and deep symbiosis between data and design. In the long run, the development of AIGC in footwear design should not stop at being an efficiency improvement tool, but should further realize the full-link data closed loop of design creativity, engineering manufacturing and market feedback, and ultimately promote the evolution of the footwear design industry in a smarter, more humanistic and more sustainable direction.