  Inverse kinematic (IK) methods recover the parameters of the joints, given
the desired position of selected elements in the kinematic chain. While the
problem is well-defined and low-dimensional, it has to be solved rapidly,
accounting for multiple possible solutions. In this work, we propose a neural
IK method that employs the hierarchical structure of the problem to
sequentially sample valid joint angles conditioned on the desired position and
on the preceding joints along the chain. In our solution, a hypernetwork $f$
recovers the parameters of multiple primary networks {$g_1,g_2,\dots,g_N$,
where $N$ is the number of joints}, such that each $g_i$ outputs a distribution
of possible joint angles, and is conditioned on the sampled values obtained
from the previous primary networks $g_j, j<i$. The hypernetwork can be trained
on readily available pairs of matching joint angles and positions, without
observing multiple solutions. At test time, a high-variance joint distribution
is presented, by sampling sequentially from the primary networks. We
demonstrate the advantage of the proposed method both in comparison to other IK
methods for isolated instances of IK and with regard to following the path of
the end effector in Cartesian space.
