Plausible Pose

Plausible pose estimation focuses on accurately and reliably determining the 3D position and orientation of objects or humans, particularly in challenging scenarios with occlusions, noise, or limited sensor data. Current research emphasizes improving the accuracy and robustness of pose estimation through techniques like incorporating uncertainty quantification, leveraging multi-modal data (RGB-D, WiFi, EM sensors), and employing advanced model architectures such as diffusion models, neural distance fields, and graph neural networks to generate physically and anatomically plausible poses. This work is crucial for advancing applications in robotics, augmented/virtual reality, human-computer interaction, and clinical monitoring, where accurate and reliable pose information is essential for safe and effective operation.