NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D
  Human Pose and Shape Estimation
Jiefeng Li Siyuan Bian Qi Liu Jiasheng Tang Fan Wang Cewu Lu
With the progress of 3D human pose and shape estimation, state-of-the-art
methods can either be robust to occlusions or obtain pixel-aligned accuracy in
non-occlusion cases. However, they cannot obtain robustness and mesh-image
alignment at the same time. In this work, we present NIKI (Neural Inverse
Kinematics with Invertible Neural Network), which models bi-directional errors
to improve the robustness to occlusions and obtain pixel-aligned accuracy. NIKI
can learn from both the forward and inverse processes with invertible networks.
In the inverse process, the model separates the error from the plausible 3D
pose manifold for a robust 3D human pose estimation. In the forward process, we
enforce the zero-error boundary conditions to improve the sensitivity to
reliable joint positions for better mesh-image alignment. Furthermore, NIKI
emulates the analytical inverse kinematics algorithms with the twist-and-swing
decomposition for better interpretability. Experiments on standard and
occlusion-specific benchmarks demonstrate the effectiveness of NIKI, where we
exhibit robust and well-aligned results simultaneously. Code is available at
https://github.com/Jeff-sjtu/NIKI