Neural Abstract Style Transfer for Chinese Traditional Painting
This addresses the problem of applying neural style transfer to Chinese traditional painting, which is visually abstract and textureless, for researchers and practitioners in computer vision and digital art, representing an incremental advancement in a niche domain.
The paper tackles the challenge of preserving abstraction in neural style transfer for Chinese traditional painting, proposing a method that uses an MXDoG-guided filter and differentiable loss terms, and shows more appealing stylized results than state-of-the-art methods.
Chinese traditional painting is one of the most historical artworks in the world. It is very popular in Eastern and Southeast Asia due to being aesthetically appealing. Compared with western artistic painting, it is usually more visually abstract and textureless. Recently, neural network based style transfer methods have shown promising and appealing results which are mainly focused on western painting. It remains a challenging problem to preserve abstraction in neural style transfer. In this paper, we present a Neural Abstract Style Transfer method for Chinese traditional painting. It learns to preserve abstraction and other style jointly end-to-end via a novel MXDoG-guided filter (Modified version of the eXtended Difference-of-Gaussians) and three fully differentiable loss terms. To the best of our knowledge, there is little work study on neural style transfer of Chinese traditional painting. To promote research on this direction, we collect a new dataset with diverse photo-realistic images and Chinese traditional paintings. In experiments, the proposed method shows more appealing stylized results in transferring the style of Chinese traditional painting than state-of-the-art neural style transfer methods.