Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICIP42928.2021.9506379 | ICIP |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi Song Liu | 1 | 0 | 1.35 |
| Vicky Kalogeiton | 2 | 0 | 0.68 |
| Cani Marie-Paule | 3 | 0 | 0.68 |