Skinned Multi Person Linear Model

Skinned Multi-Person Linear Models (SMPLs) are used to represent human body kinematics, aiming to accurately capture and analyze human movement from various data sources like video and sensor readings. Current research focuses on improving the accuracy and efficiency of SMPL-based methods, employing techniques like deep learning (e.g., 3D convolutional neural networks, recurrent neural networks), Gaussian processes, and Kalman filtering to estimate joint angles, predict future motion, and incorporate anatomical constraints. This work has significant implications for diverse fields, including biomechanics, human-robot interaction, healthcare (e.g., diagnosing movement disorders), and ergonomics, enabling more accurate and efficient analysis of human movement in various contexts.