Arbitrary Style

Arbitrary style research focuses on generating outputs (images, text, 3D avatars) that match a specified style, even with limited style examples or novel, unseen styles. Current approaches leverage techniques like attention modification, adaptive instance normalization, contrastive learning, and diffusion models, often incorporating style mixing or inversion to extract and apply stylistic features. This field is significant for advancing generative models, enabling applications such as personalized content creation, improved domain adaptation in computer vision, and enhanced control over stylistic aspects in various creative and technical domains.