Hand Face Interaction

Hand-face and hand-object interaction research focuses on accurately reconstructing 3D models of these interactions from single 2D images, a challenging task due to occlusions and complex deformations. Current approaches leverage deep learning, employing transformer-based architectures and implicit neural representations (like NeRF) to simultaneously estimate hand and object/face poses, contact points, and deformations. These advancements enable more realistic and physically plausible 3D scene reconstruction, with applications in augmented and virtual reality, robotics (e.g., grasping and manipulation), and computer graphics. The development of faster, more robust, and generalizable methods remains a key focus.