Exemplar Based Image Translation

Exemplar-based image translation aims to transform an input image into a new style guided by a reference exemplar image, without requiring paired training data. Recent research focuses on improving the accuracy of cross-domain correspondences using techniques like transformers and diffusion models, while simultaneously reducing reliance on explicit semantic information through methods such as dense style representations and self-supervised learning. These advancements lead to more realistic and detailed translations, impacting fields like artistic image manipulation and image editing software by offering greater control and flexibility in style transfer.