The original work for artistic style transfer with neural networks proposed a slow optimization algorithm that works on any arbitrary painting. Subsequent work developed a method for fast artistic style transfer that may operate in real time, but was limited to one or a limited set of styles.

Style transfer is the task of producing a pastiche image 'p' that shares the content of a content image 'c' and the style of a style image 's'. A Learned Representation for Artistic Style. Vincent Dumoulin, Jon Shlens, Manjunath Kudlur.

Crossword generator

AnimeGAN improved the CVPR paper CartoonGAN, mainly to solve the over-stylized and color artifact area. For the details, you can refer to the Zhihu article writes by the paper author. Based on the AnimeGAN, the AnimeGANv2 add the total variation loss in the generator loss.

The aim of portrait cartoon stylization is to transform real photos into cartoon images with portrait's ID information and texture details. We use Generative Adversarial Network method to realize the mapping of picture to cartoon.