Abstract
In order to solve the problems of poor region delineation and boundary artifacts in Chinese style migration of images, an improved Variational Autoencoder (VAE) method for dress style migration is proposed. Firstly, the Yolo v3 model is used to quickly identify the dress localization of the input image, then, the classical semantic segmentation algorithm (FCN) is used to finely delineate the desired dress style migration region twice, and finally, the trained VAE model is used to generate the migrated Chinese style image. The results show that, compared with the traditional style migration model, the improved VAE style migration model can obtain finer synthetic images for dress style migration and can adapt to different Chinese traditional styles to meet the application requirements of dress style migration scenarios. We evaluated several deep learning-based models and achieved a BLEU value of 0.6 on average. The transformer-based model outperformed the other models, achieving a BLEU value of up to 0.72.
Highlights
In art creation, style is a specific, abstract representation of the characteristics of an artistic school
Image style migration refers to the method of extracting the content features of one image and the style features of another image separately and fusing them to generate an image with a new style [1]
In the field of computer vision, the traditional image style migration method has many drawbacks in practice, especially in the process of image style migration, which requires professional style analysis of images in advance, and mathematical modeling of abstract style features using complex and tedious mathematical formulas [2]. is is a timeconsuming and labor-intensive process, and specific image styles often need to be mathematically modeled in a specific way, but the visual results obtained are unsatisfactory, and the generality and usability of the algorithm model are extremely poor
Summary
Style is a specific, abstract representation of the characteristics of an artistic school. Compared with traditional image style migration methods, the stylized image visuals of deep learning-based image style migration methods have significant advantages in terms of texture and color. Using the deep learning approach, the high-level abstract features of images, such as image texture, image color, and image structure, can be efficiently extracted and combined in a way that is consistent with human visual habits, with excellent versatility and ease of use, eliminating the need for repetitive and tedious mathematical modeling processes [5]. Erefore, how to improve the computational efficiency of deep learning-based image style migration methods, enhance the visual effect of stylized images, and maximize the scale of compressed model parameters is an important research hotspot, which is important for the promotion of its commercial application.
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