3D Human Face

Research on 3D human face modeling focuses on creating realistic and versatile digital representations from various input sources, aiming for high-fidelity geometry and photorealism. Current efforts leverage generative adversarial networks (GANs), diffusion models, and neural radiance fields (NeRFs), often incorporating self-supervised learning and multi-view consistency techniques to improve accuracy and generalizability. These advancements are driving progress in applications such as face recognition, virtual and augmented reality, and animation, by enabling the creation of high-quality synthetic face data and facilitating more sophisticated analysis of facial features and expressions. The development of perceptually-driven loss functions further enhances the realism and accuracy of these 3D models.