Hand Reconstruction

Hand reconstruction aims to create accurate 3D models of hands from various input modalities, primarily images and videos, for applications in human-computer interaction, virtual reality, and robotics. Current research heavily utilizes deep learning, focusing on transformer-based architectures and graph convolutional networks to model hand geometry, pose, and texture, often incorporating techniques like inverse rendering and multi-view fusion to improve accuracy and handle occlusions. These advancements are significant because they enable more natural and intuitive interactions with computers and robots, and provide valuable tools for studying human hand biomechanics and movement.