ML Researchers Propose A Novel AI Model To Effectively Handle The Texture-Age Evolution

A lifespan face synthesis model aims to create a set of photo-realistic images that show what someone's whole life would look like, given just one picture as a reference. The generated image is expected to be age-sensitive with realistic transformations in shape and texture while maintaining their identity. This task is challenging because faces undergo separate but highly nonlinear changes when it comes to how they change due to ageing; for example, skin loses elasticity which can make them appear wrinkled or saggy more quickly than other parts of your body might start changing. The latest LFS (lifespan face synthesis) models are based on the new generative adversarial networks that use conditional transformations to allow people's age code to be seen. They have significantly benefitted from recent advancements of GANs, and they're still improving every day.