  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.
