Texture Transfer

Texture transfer aims to seamlessly replicate the texture of one image onto another, preserving the underlying structure of the target image. Current research focuses on improving the fidelity and efficiency of this process, employing techniques like diffusion models, transformers, and convolutional neural networks within various architectures (e.g., plug-and-play modules for existing neural rendering systems, or dedicated networks for specific tasks like virtual try-on). These advancements are significant for applications ranging from image editing and 3D modeling to enhancing the realism of synthetic data for tasks such as morphing attack detection and improving the efficiency of hyperspectral image processing.