Style transfer is the process of rendering one image with some content in the style of another image, representing the style. Recent studies of (2017) have shown significant improvement of style transfer rendering quality by adjusting traditional methods of (2016) and (2016) with regularizer, forcing preservation of the depth map of the content image. However these traditional methods are either computationally inefficient or require training a separate neural network for new style. AdaIN method of (2017) allows efficient transferring of arbitrary style without training a separate model but is not able to reproduce the depth map of the content image. We propose an extension to this method, allowing depth map preservation. Qualitative analysis and results of user evaluation study indicate that the proposed method provides better stylizations, compared to the original style transfer methods of (2016) and (2017).
The other arbitrary real time method was proposed by Li @cite_16 , so-called style transfer. This approach considered pretrained deep convolution encoder (they also used VGG-19 architecture) for high-features extraction and decoders, which can reconstruct images from hidden representation from different layers of VGG. For style transfer they applied whitening and linear transformation which imposed the style, represented as mean vector and covariance matrix of the transformation. Stylization results were obtained after passing hidden representations through the trained decoders.
@cite_16
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Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures by simple feature coloring.