Multiple Style Transfer via Variational AutoEncoder
This addresses the challenge of efficiently mixing multiple artistic styles in image generation, though it appears incremental as it builds on existing style transfer techniques.
The paper tackles the problem of multiple style transfer by proposing ST-VAE, a Variational AutoEncoder that projects styles to a linear latent space for merging via interpolation, resulting in a method that outperforms others in speed and flexibility.
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.