Image Style Transfer

Image style transfer studies how to change an image’s appearance—such as its brushwork, color, texture, or makeup—while retaining important content like objects, faces, and layout. Methods range from feed-forward neural networks and color transforms to pretrained diffusion models controlled through attention, feature alignment, adapters, masks, or optimization. Current work emphasizes fine-grained regional control, multiple style references, and applications such as video, 3D scenes, and medical imaging. A central challenge is balancing faithful style matching with content preservation and limited semantic leakage, while evaluation metrics still do not fully capture human judgments of artistic similarity.