2021/10/13 by Zhi-Song Liu, Vicky Kalogeiton, Liu, Zhi-Song +4 · 3 citations
Computer Science · Engineering · #Advanced Image Processing Techniques #Algorithm #Art #Artificial intelligence #Artificial neural network #Autoencoder #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Focus (optics) #Generative Adversarial Networks and Image Synthesis #Image Enhancement Techniques #Image and Video Processing (eess.IV) #Information retrieval #Merge (version control) #Space (punctuation) #Style (visual arts) #Transfer (computing) #Visual arts #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2110.07375
published in arXiv (Cornell University) (Cornell University) · 5 papges, 4 figures
arxiv created 2021/10/13 · openalex publication_date 2021/10/13 · arxiv updated 2021/10/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.