  We propose a recurrent neural network architecture with a Forward Kinematics
layer and cycle consistency based adversarial training objective for
unsupervised motion retargetting. Our network captures the high-level
properties of an input motion by the forward kinematics layer, and adapts them
to a target character with different skeleton bone lengths (e.g., shorter,
longer arms etc.). Collecting paired motion training sequences from different
characters is expensive. Instead, our network utilizes cycle consistency to
learn to solve the Inverse Kinematics problem in an unsupervised manner. Our
method works online, i.e., it adapts the motion sequence on-the-fly as new
frames are received. In our experiments, we use the Mixamo animation data to
test our method for a variety of motions and characters and achieve
state-of-the-art results. We also demonstrate motion retargetting from
monocular human videos to 3D characters using an off-the-shelf 3D pose
estimator.
