A Sustainable Framework for Realism Evaluation and Optimization of Virtual Fabric Drape Effect

As awareness of the negative environmental impact of fashion grows, most companies are choosing to innovate in areas such as recycling and digital transformation. In the context of the rising digital economy and the ongoing development of 3D simulation software, there has been a notable increase in the demand for realistic 3D virtual-fitting effects. However, no standardized evaluation method exists for the realism of virtual fabric drape. This study proposes a systematic approach to enhance the objective evaluation and rapid optimization of virtual fabric drape realism. The research is structured in four stages. First, virtual drape testing conditions are established by referencing real-world fabric drape tests. Second, fuzzy classification is employed to categorize the realism of virtual drape effects into six levels. Third, subjective evaluations of representative fabrics are conducted to define the grading thresholds and reveal differences among the fabric types. Finally, a backpropagation (BP) neural network is used to construct three rapid evaluation models and one optimization model, which are validated through practical application. The proposed method supports accurate assessment and optimization of virtual simulations, contributes to a refined virtual fabric database, and offers insights for improving other 3D fitting software.