  We present SLoMo: a first-of-its-kind framework for transferring skilled
motions from casually captured "in the wild" video footage of humans and
animals to legged robots. SLoMo works in three stages: 1) synthesize a
physically plausible reconstructed key-point trajectory from monocular videos;
2) optimize a dynamically feasible reference trajectory for the robot offline
that includes body and foot motion, as well as contact sequences that closely
tracks the key points; 3) track the reference trajectory online using a
general-purpose model-predictive controller on robot hardware. Traditional
motion imitation for legged motor skills often requires expert animators,
collaborative demonstrations, and/or expensive motion capture equipment, all of
which limits scalability. Instead, SLoMo only relies on easy-to-obtain
monocular video footage, readily available in online repositories such as
YouTube. It converts videos into motion primitives that can be executed
reliably by real-world robots. We demonstrate our approach by transferring the
motions of cats, dogs, and humans to example robots including a quadruped (on
hardware) and a humanoid (in simulation). To the best knowledge of the authors,
this is the first attempt at a general-purpose motion transfer framework that
imitates animal and human motions on legged robots directly from casual videos
without artificial markers or labels.
