From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging

Face aging has become a crucial task in computer vision, with applications ranging from entertainment to healthcare. However, existing methods struggle with achieving a realistic and seamless transformation across the entire lifespan, especially when handling large age gaps or extreme head poses. The core challenge lies in balancing $age\ accuracy$ and $identity\ preservation$--what we refer to as the $Age\text{-}ID\ trade\text{-}off$.