Parameter-Free Style Projection for Arbitrary Style Transfer
This addresses the challenge of preserving content and style details in style transfer for applications in image editing and computer vision, representing an incremental improvement over existing methods.
The paper tackles the problem of arbitrary image style transfer by proposing a new feature-level style transformation technique called Style Projection, which achieves parameter-free, fast, and effective content-style fusion, as demonstrated through qualitative analysis, quantitative evaluation, and user studies.
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.