Human Face
Human face research spans diverse fields, aiming to understand and replicate facial features, expressions, and their associated social and psychological implications. Current research focuses on developing advanced generative models (e.g., StyleGAN, diffusion models) for realistic 3D face generation from single images and audio, often incorporating techniques like mesh attention and conditional flow matching to enhance realism and control. These advancements have significant implications for applications ranging from virtual reality and animation to forensic science and the detection of AI-generated media, while also raising ethical concerns regarding privacy and potential misuse.
Papers
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
Marcel C. Bühler, Kripasindhu Sarkar, Tanmay Shah, Gengyan Li, Daoye Wang, Leonhard Helminger, Sergio Orts-Escolano, Dmitry Lagun, Otmar Hilliges, Thabo Beeler, Abhimitra Meka
Decaf: Monocular Deformation Capture for Face and Hand Interactions
Soshi Shimada, Vladislav Golyanik, Patrick Pérez, Christian Theobalt