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PARAMETER-FREE STYLE PROJECTION FOR ARBITRARY IMAGE STYLE TRANSFER

Siyu Huang, Haoyi Xiong, Qingzhong Wang, Zeyu Chen, Dejing Dou, Tianyang Wang, Bihan Wen, Jun Huan

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    Length: 00:10:50
09 May 2022

Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on arbitrary style images. In this task the feature-level content-style transformation plays a vital role for proper fusion of features. Existing feature transformation algorithms often suffer from loss of content or style details, non-natural stroke patterns, and unstable training. To mitigate these issues, this paper proposes a new feature-level style transformation technique, named Style Projection, for parameter-free, fast, and effective content-style transformation. This paper further presents a real-time feed-forward model to leverage Style Projection for arbitrary image style transfer, which includes a regularization term for matching the semantics between input contents and stylized outputs. Extensive qualitative analysis, quantitative evaluation, and user study have demonstrated the effectiveness and efficiency of the proposed methods.