Pose Control

Pose control, the ability to precisely manipulate the position and orientation of objects or virtual entities, is a central challenge across robotics, computer vision, and computer graphics. Current research focuses on improving the accuracy and robustness of pose estimation and control using diverse methods, including diffusion models, neural networks (e.g., ControlNet, graph neural networks), and model predictive control, often incorporating visual feedback from cameras or depth sensors. These advancements are driving progress in applications ranging from robot manipulation and autonomous navigation to realistic avatar creation and human-computer interaction.