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 ST-VAE, a Variational AutoEncoder for latent space-based style transfer. It performs multiple style transfer by projecting nonlinear styles to a linear latent space, enabling to merge styles via linear interpolation before transferring the new style to the content image. To evaluate ST-VAE, we experiment on COCO for single and multiple style transfer. We also present a case study revealing that ST-VAE outperforms other methods while being faster, flexible, and setting a new path for multiple style transfer.
@article{arxiv.2110.07375,
title = {Multiple Style Transfer via Variational AutoEncoder},
author = {Zhi-Song Liu and Vicky Kalogeiton and Marie-Paule Cani},
journal= {arXiv preprint arXiv:2110.07375},
year = {2021}
}