Pose, Kinematics, and Dynamics

Recovering accurate 3D human pose and shape from monocular input remains a challenging problem despite the rapid advancements powered by deep neural networks. Existing methods have limitations in achieving both robustness and mesh-image alignment, and the estimated pose suffers from physical artifacts such as foot sliding and body leaning. In this talk, we present two new methods to address these limitations. Firstly, we introduce NIKI, an inverse kinematics algorithm that utilizes an invertible neural network to model both the forward kinematics process and the inverse kinematics process. With the explict bi-directional error modeling, NIKI achieves pixel-aligned estimation accuracy and is robust to occlusions. Secondly, we present a new method, named D&D, that utilizes human body dynamics to generate physically plausible body motion and global trajectories in varied scenarios.