Multiple Style Transfer Via Variational Autoencoder - École polytechnique Access content directly
Conference Papers Year : 2021

Multiple Style Transfer Via Variational Autoencoder


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.
Fichier principal
Vignette du fichier
ICIP-2021-STVAE.pdf (32.47 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03353538 , version 1 (24-09-2021)



Zhi-Song Liu, Vicky Kalogeiton, Marie-Paule Cani. Multiple Style Transfer Via Variational Autoencoder. 2021 IEEE International Conference on Image Processing (ICIP), Sep 2021, Anchorage, Alaska (virtual), United States. ⟨10.1109/ICIP42928.2021.9506379⟩. ⟨hal-03353538⟩
112 View
351 Download



Gmail Facebook X LinkedIn More