Photorealistic Style Transfer

Photorealistic style transfer aims to seamlessly transfer the artistic style of one image onto another, preserving photorealism and avoiding unrealistic artifacts. Current research focuses on developing efficient and effective methods, including feed-forward networks, lightweight models leveraging techniques like adaptive instance normalization and color MLPs, and approaches utilizing 3D neural radiance fields (NeRFs) for consistent stylization of 3D scenes and videos. These advancements address limitations in speed, memory usage, and multi-view consistency, improving the quality and applicability of style transfer across various media. The resulting techniques have significant implications for image and video editing, 3D modeling, and other visual media applications.