The fast fashion industry has been widely criticized for its substantial consumption of resources and significant environmental pollution. In contrast, virtual fashion clothes are attracting attention from consumers and academics for their notable sustainability benefits and potential for fashion innovation. However, research on consumer acceptance of virtual clothes and the role of sustainability remains limited. This study aims to fill this gap by applying the Emotional Three-Level Theory to identify key virtual fashion attributes, including aesthetic, reality, personalization, presentation, sustainability, and inclusivity features, and evaluating their impact on acceptance using the Technology Acceptance Model (TAM). A survey of 503 Generation Z consumers in China, analyzed through structural equation modeling, reveals that perceived enjoyment, usefulness, and ease of use significantly influence the intention to adopt virtual fashion clothes. Aesthetic and realistic features enhance enjoyment, while personalization and presentation improve usefulness and ease of use. Sustainability features positively impact all three factors, promoting consumer acceptance. These findings offer theoretical insights for virtual fashion research and practical guidance for the fashion industry to leverage virtual technologies for environmental sustainability. Notably, the study emphasizes the potential of virtual clothes in promoting sustainable development in the fashion industry.

Standard minute value serves as a pivotal metric guiding the arrangement and balancing of production cycles in clothing production lines, and plays a crucial role in cost pricing and production order arrangement for clothing products. Given the complexity of the garment sewing process, ten influencing factors including fabric weight, fabric thickness, fabric density, stitching length, stitching shapes, cut pieces numbers, notch numbers, sewing technologies, sewing machine, and auxiliary accessories were identified. Upon this foundation, a standard sewing time prediction model, Improved particle swarm optimization - Back-propagation neural network (IPSO-BP), was proposed, focusing on non-quantitative factors. The IPSO-BP model was trained using actual sewing data from a women’s clothing production company. Compared to the unoptimized BP neural network, the IPSO-BP model demonstrated significant advantages in terms of convergence speed and prediction accuracy. Therefore, the IPSO-BP model proposed in this study holds promise for predicting standard sewing hours effectively.

This paper aims to introduce an innovative pattern-making technique to enhance the fit of pants for young females. Utilizing a 3D scanning system, seventy-one sets of detailed body data were collected to create a Y-body type mannequin. A raw pant pattern was crafted by unfolding the surfaces of the research mannequin in CLO3D. Through comprehensive assessments conducted by participants and experts for fit, movement adaptability, silhouette, and body shape correction, adjustments were made to the ease allowance and silhouette, culminating in the development of the pant pattern and final pant specification sheet that achieved a high level of satisfaction.