In order to solve the problem of attributes missing or redundant in the current clothing image attribute editing models of generate images,a design method based on Fashion-AttGAN model was conducted to transform the details of women's tops.This paper optimized feature network and added structure similarity index measure to the reconstructed loss function to improve the attribute editing ability of generator.The CP-VTON dataset was used for training to ultimately achieve fine-grained editing of women's tops sleeve length and color.The experimental results show that the generated image achieves the improvement in sleeve coherence and color accuracy,the improved model is shown to move more smoothly towards convergence trend,the reconstructed image structure similarity index measure realizes the growth of 27.4％and peak signal-to-noise ratio grows by 2.8％.The proposed model reduces attributes missing or redundant in generated images and provides a technical reference for its detail transformation.
维普
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