Identity Stylization

Identity stylization focuses on transferring the stylistic characteristics of a reference image or text prompt onto a target image or 3D model while preserving the target's original identity or content. Current research emphasizes developing methods that effectively disentangle style and content, employing techniques like diffusion models, GANs, and optimization-based approaches with architectures such as hyper-networks and low-rank adaptations for efficient fine-tuning. This field is significant for its potential applications in diverse areas, including artistic creation, virtual and augmented reality, and user-generated content personalization, offering new tools for creative expression and content manipulation.