Design method of garment effect drawing based on DCGAN algorithm
In order to improve the efficiency of clothing design and adapt to the trend of accelerating fashion product iteration,a method of generating fashion illustration based on deep convolutional Adversarial network(DCGAN)was proposed.By building a DCGAN model suitable for the task of fashion illustration generating,making a clothing show data set for model training and fashion illustration generating,the designer subjectively selects the generated fashion illustration with design reference value,calculates the proportion of effectively generated images,and evaluates the performance of the model and the quality of generated images.Through human-computer interaction,the optimized part generates images and forms the final design scheme.The results show that the optimized DCGAN model can quickly extract fashion trends and generate creative design schemes,assist designers to efficiently complete the expression of design effects,and provide effective ways and methods for the intelligent fashion design.
deep learningconvolutional neural networkDCGANeffect drawinginteraction design
NETL
NSTL
万方数据
维普
