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.

    @InProceedings{Liu21,
                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",
              }