Neural Style Transfer

Neural style transfer (NST) is a technique that modifies the visual style of an image or video while preserving its content, achieving artistic effects by transferring the stylistic characteristics of one image onto another. Current research focuses on extending NST to diverse data types, including 3D models, videos, and even time series, often employing architectures like generative adversarial networks (GANs), diffusion models, and hypernetworks to achieve high-quality and controllable stylization. This field is significant for its applications in various domains, such as enhancing user experience in autonomous driving, improving object detection in challenging conditions, and creating novel artistic expressions in computer graphics and digital art.