Core Training: Learning Deep Neuromuscular Control of the Torso for Anthropomimetic Animation

Download or read book Core Training: Learning Deep Neuromuscular Control of the Torso for Anthropomimetic Animation written by tao zhou and published by . This book was released on 2019 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.