  This paper introduces V-SysId, a novel method that enables simultaneous
keypoint discovery, 3D system identification, and extrinsic camera calibration
from an unlabeled video taken from a static camera, using only the family of
equations of motion of the object of interest as weak supervision. V-SysId
takes keypoint trajectory proposals and alternates between maximum likelihood
parameter estimation and extrinsic camera calibration, before applying a
suitable selection criterion to identify the track of interest. This is then
used to train a keypoint tracking model using supervised learning. Results on a
range of settings (robotics, physics, physiology) highlight the utility of this
approach.
