Style Transfer

Style transfer aims to modify the visual or auditory style of data (images, audio, 3D scenes, text) while preserving its content. Current research focuses on developing efficient and controllable style transfer methods, employing architectures like diffusion models, neural radiance fields, transformers, and Gaussian splatting, often incorporating techniques like attention mechanisms and optimization-based approaches to achieve training-free or few-shot learning. These advancements are impacting diverse fields, including image editing, 3D modeling, audio processing, and natural language processing, by enabling more creative control and efficient manipulation of multimedia data. The development of high-quality, controllable style transfer methods is crucial for applications ranging from artistic expression to medical image analysis.