Modern works on style transfer focus on transferring style from a single image.
Recently, some approaches study multiple style transfer; these, however, are either too slow or fail to mix multiple styles. We propose \net, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, and it fuses different styles by linear interpolation and transfers the new style to the content image.
To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. Moreover, we present a case study revealing that ST-VAE outperforms other methods while being faster, flexible, and setting a new path for multiple style transfer.
author = "Liu, Z.~S. and Kalogeiton, V. and Cani, M.~P.",
title = "Multiple Style Transfer via Variational AutoEncoder",
booktitle = "IEEE International Conference on Image Processing (ICIP)",
year = "2021",
This work was supported supported by the the Google chair at École Polytechnique.