Hand Object Reconstruction

Hand-object reconstruction aims to accurately model the 3D geometry and pose of hands interacting with objects from visual input, often addressing challenges like occlusion and contact modeling. Current research heavily utilizes graph neural networks, diffusion models, and neural radiance fields to achieve this, focusing on improving the realism and physical plausibility of reconstructions, often incorporating geometric reasoning and natural language descriptions for control. This field is crucial for advancing human-computer interaction, robotics (particularly in areas like handover and manipulation), and virtual/augmented reality applications by enabling more natural and intuitive interactions with digital environments.