Despite its importance, the core of the human body has to date
received inadequate attention in the computer graphics literature. We
tackle the challenge of biomechanically simulating and controlling the
torso, including of course the spine, in its full musculoskeletal
complexity, thus providing a whole-body biomechanical human model with
a full set of articular degrees of freedom actuated by many hundreds
of muscles embedded in a finite-element soft tissue simulation.
Performing skillful (non-locomotive) motor tasks while bipedally
balancing upright in gravity has never before been attempted with a
musculoskeletal model of such realism and complexity. Our approach to
tackling the challenge is machine learning, specifically deep
learning. The neuromuscular motor control system of our virtual human
comprises 12 trained deep neural networks (DNNs), including a core
voluntary/reflex DNN pair devoted to innervating the 443 muscles of
the torso. By synthesizing its own training data offline, our virtual
human automatically learns efficient, online, active control of the
core musculoskeletal complex as well as its proper coordination with
the five extremities---the cervicocephalic, arm, and leg
musculoskeletal complexes---in order to perform nontrivial motor tasks
such as sitting and standing, doing calisthenics, stepping, and golf
putting. Moreover, we equip our virtual human with a full sensorimotor
control system, thus making it autonomous. Afforded suitable NN-based
machine perception, our model can also visually analyze drawings and
manually sketch similar drawings as it balances in an upright stance
before a large touchscreen display.