Parameter-Free Style Projection for Arbitrary Style Transfer

TL;DR

This paper introduces Style Projection, a parameter-free, real-time method for arbitrary style transfer that preserves content and style details while producing natural strokes.

Contribution

It proposes a novel feature-level style transformation technique called Style Projection, enabling fast, stable, and parameter-free arbitrary style transfer.

Findings

Outperforms existing methods in preserving details and naturalness

Achieves real-time processing speeds

Demonstrates superior qualitative and quantitative results

Abstract

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.