Facebook and Google's AI generates 3D human poses

Predicting 3D human poses might not fall within most people's purview, but robotics, computer graphics, and other fields chiefly concerned with kinematics -- the branch of mechanics concerned with the motion of objects -- stand to benefit from systems that can do just that. Pose prediction is a task to which artificial intelligence (AI) has been applied before, somewhat recently by Google, but some prior work hit a roadblock: It stretched digital joints and bones in unnatural directions, particularly when the joints rotated. New research by Facebook's AI Research division, Google Brain, and ETH Zurich promises to address the problem, fortunately. In a paper ("Modeling Human Motion with Quaternion-based Neural Networks") published on the preprint server Arxiv.org this week, researchers describe an AI system -- QuaterNet -- that improves pose generation by representing joint rotations as complex number systems called quaternions, and by penalizing joint position errors. As the coauthors of the paper explain, recurrent neural networks -- a type of AI algorithm capable of learning long-term dependencies -- have been historically used to perform both short- and long-term pose prediction, while convolutional neural networks -- algorithms highly adept at analyzing visual imagery -- have been successfully applied to long-term generation of locomotion (movement from one place to another).