Generative Image Editing

Generative image editing focuses on modifying existing images using AI models, aiming for high-quality, consistent results that precisely reflect user intentions. Current research emphasizes improving the fidelity and efficiency of diffusion-based models, addressing challenges like maintaining image coherence and accurately following complex instructions, often through novel feedback learning strategies and multi-modal conditioning (e.g., text and reference images). These advancements are significant for various applications, including biomedical image analysis (e.g., simulating dataset shifts for model robustness testing) and creative design (e.g., enabling realistic 3D object manipulation within 2D images), ultimately enhancing the capabilities and reliability of image manipulation tools.