Hand Motion

Hand motion research focuses on accurately capturing, analyzing, and synthesizing human hand movements for diverse applications, including human-computer interaction, robotics, and healthcare. Current research emphasizes developing robust and efficient methods for 3D hand pose estimation and reconstruction using various sensor modalities (e.g., cameras, gloves, ultrasound) and machine learning architectures like transformers, diffusion models, and Gaussian processes. These advancements are improving the accuracy and realism of hand motion capture and generation, leading to significant improvements in areas such as virtual reality, assistive robotics, and ergonomic risk assessment.