GAN Generated Face

Generative adversarial networks (GANs) are increasingly used to generate realistic synthetic faces, with research focusing on improving image quality, controlling specific facial attributes (like expression and pose), and detecting GAN-generated forgeries. Current efforts employ various architectures, including convolutional GANs, 3D Morphable Model (3DMM)-conditioned GANs, and hybrid models combining GANs with other techniques like forests or diffusion models, to achieve higher fidelity and better control over the generated output. This field is significant due to its implications for applications such as facial expression recognition, biometric security, and the creation of realistic synthetic data for training other AI models, while also raising concerns about the potential for misuse in creating deepfakes.