Gaisbauer, FelixFroehlich, PhilippLehwald, JannesAgethen, PhilippRukzio, EnricoJain, Eakta and Kosinka, Jirí2018-04-142018-04-1420181017-4656https://doi.org/10.2312/egp.20181010https://diglib.eg.org:443/handle/10.2312/egp20181010Motion blending and character animation systems are widely used in different domains such as gaming or simulation within production industries. Most of the established approaches are based on motion blending techniques. These approaches provide natural motions within common scenarios while inducing low computational costs. However, with increasing amount of influence parameters and constraints such as collision-avoidance, they increasingly fail or require a vast amount of time to meet these requirements. With ongoing progress in artificial intelligence and neural networks, recent works present deep learning based approaches for motion synthesis, which offer great potential for modeling natural motions, while considering heterogeneous influence factors. In this paper, we propose a novel deep blending approach to simulate non-cyclical natural reach motions based on an extension of phase functioned deep neural networks.Computing methodologiesCollision detectionSimulation types and techniquesAnimation10.2312/egp.201810105-6