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Poster
Universal Style Transfer via Feature Transforms
Yijun Li · Chen Fang · Jimei Yang · Zhaowen Wang · Xin Lu · Ming-Hsuan Yang

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #81
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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.

Keywords: Computer Vision · Applications

Author Information

Yijun Li (University of California, Merced)
Chen Fang (Adobe Research)
Jimei Yang (Adobe Research)
Zhaowen Wang (Adobe Research)
Xin Lu (Adobe)
Ming-Hsuan Yang (UC Merced / Google)

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