Multiple Style Transfer via Variational AutoEncoder

• URL: http://arxiv.org/abs/2110.07375v1
• Date: Wed, 13 Oct 2021 06:47:13 GMT
• Title: Multiple Style Transfer via Variational AutoEncoder
• Authors: Zhi-Song Liu and Vicky Kalogeiton and Marie-Paule Cani
• Abstract summary: 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 before transferring the new style to the content image.
• Score: 16.797476504327665
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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