Neural Style Transfer: A Review and Analysis
Jiacheng Cao · Highlights in Science Engineering and Technology · 2025
Neural style transfer (NST) is a fascinating deep learning application that merges the style of one image with the content of another, resulting in visually compelling artistic images. This article reviews multiple research advances in the field of NST, including new style loss based on Sliced Wasserstein distance, VTNet model for video style conversion, Graph Neural Network based image style transfer method, multi style geometric deformation method, 3D neural style conversion, style transfer for 3D grids, traditional Chinese painting style transfer, and artistic conversion of QR codes. Through these studies, the author aims to improve the effectiveness and efficiency of image and video style conversion while maintaining the integrity of content information. The newly proposed style loss function utilizes SWD to improve computational efficiency and theoretically ensures the accuracy of style similarity. VTNet introduces prediction and generation branches to achieve high-quality, real-time video style conversion. In addition, the article also explores semi parametric methods based on GNN, multi style geometric transformations, and style transfer techniques for specific cultural artworks, demonstrating the potential application of neural style transfer in different fields and its contribution to artistic creation and design.