Head Motion

Head motion analysis is a burgeoning field focusing on understanding and modeling human head movements across diverse contexts, with primary objectives including accurate estimation, prediction, and generation of head poses and trajectories. Current research employs various machine learning models, such as convolutional neural networks (CNNs), Long Short-Term Memory (LSTM) networks, and transformers, often incorporating techniques like diffusion models and variational autoencoders, to achieve these goals. This research has significant implications for improving virtual and augmented reality experiences, enhancing medical imaging techniques (e.g., reducing motion artifacts in CT scans), and developing more natural and intuitive human-robot interaction systems.