Learning articulated object pose is inherently difficult because the pose is
high dimensional but has many structural constraints. Most existing work do not
model such constraints and does not guarantee the geometric validity of their
pose estimation, therefore requiring a post-processing to recover the correct
geometry if desired, which is cumbersome and sub-optimal. In this work, we
propose to directly embed a kinematic object model into the deep neutral
network learning for general articulated object pose estimation. The kinematic
function is defined on the appropriately parameterized object motion variables.
It is differentiable and can be used in the gradient descent based optimization
in network training. The prior knowledge on the object geometric model is fully
exploited and the structure is guaranteed to be valid. We show convincing
experiment results on a toy example and the 3D human pose estimation problem.
For the latter we achieve state-of-the-art result on Human3.6M dataset.

本文介绍了一种使用动力学模型来辅助深度学习神经网络进行关节式物体姿态估计的方法，并在玩具和人类 3D 姿势问题的数据实验中证明了该方法的有效性。