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<p>The task of image style transfer is to automatically redraw an input image in the style of another image, such as an artist’s painting. The disadvantage of conventional stylization algorithms is the uniqueness of result. If the user is not satisfied with the way the style was transferred, he has no option to remake the stylization. The paper provides an overview of existing style transfer methods that generate diverse results after each run and proposes two new methods. The first method enables diversity by concatenating a random vector into inner image representation inside the neural network and by reweighting image features accordingly in the loss function. The second method allows diverse stylizations by passing the stylized image through orthogonal transformations, which impact the way the target style is transferred. These blocks are trained to replicate patterns from additional pattern images, which serve as additional input and provide an interpretable way to control stylization variability for the end user. Qualitative and quantitative comparisons demonstrate that both methods are capable to generate different stylizations with higher variability achieved by the second method. The code of both methods is available on github.</p>

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Diversified image style transfer—approaches, new methods and directed variability control

  • 期刊论文

  • 发表日期：

    2025年1月17日

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  • Alexander Ustyuzhanin,
  • Victor Kitov,
  • Vladimir Kitov

摘要

The task of image style transfer is to automatically redraw an input image in the style of another image, such as an artist’s painting. The disadvantage of conventional stylization algorithms is the uniqueness of result. If the user is not satisfied with the way the style was transferred, he has no option to remake the stylization. The paper provides an overview of existing style transfer methods that generate diverse results after each run and proposes two new methods. The first method enables diversity by concatenating a random vector into inner image representation inside the neural network and by reweighting image features accordingly in the loss function. The second method allows diverse stylizations by passing the stylized image through orthogonal transformations, which impact the way the target style is transferred. These blocks are trained to replicate patterns from additional pattern images, which serve as additional input and provide an interpretable way to control stylization variability for the end user. Qualitative and quantitative comparisons demonstrate that both methods are capable to generate different stylizations with higher variability achieved by the second method. The code of both methods is available on github.