Style Harmonization

Style harmonization focuses on seamlessly integrating disparate visual styles within an image or across images, aiming for natural-looking results. Current research emphasizes efficient algorithms, often leveraging diffusion models and attention mechanisms, to achieve this, with a focus on minimizing computational cost and improving the fidelity of style transfer. These advancements are relevant to various applications, including image editing, autonomous driving (improving robustness to weather effects), and self-supervised learning (enhancing domain generalization capabilities). The ultimate goal is to create robust and efficient methods for manipulating and combining visual styles in diverse contexts.

Papers