EG 2018 - Posters

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Browsing EG 2018 - Posters by Subject "Collision detection"

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    Introducing a Modular Concept for Exchanging Character Animation Approaches
    (The Eurographics Association, 2018) Gaisbauer, Felix; Agethen, Philipp; Bär, Thomas; Rukzio, Enrico; Jain, Eakta and Kosinka, Jirí
    Nowadays, motion synthesis and character animation systems are used in different domains ranging from gaming to medicine and production industries. In recent years, there has been a vast progress in terms of realistic character animation. In this context, motion-capture based animation systems are frequently used to generate natural motions. Other approaches use physics based simulation, statistical models or machine learning methods to generate realistic motions. These approaches are however tightly coupled with the development environment, thus inducing high porting efforts if being incorporated into different platforms. Currently, no standard exists which allows to exchange complex character animation approaches. A comprehensive simulation of complex scenarios utilizing these heterogeneous approaches is therefore not possible, yet. In a different domain than motion, the Functional Mock-up Interface standard has already solved this problem. Initially being tailored to industrial needs, the standards allows to exchange dynamic simulation approaches such as solvers for mechatronic components. We present a novel concept, extending this standard to couple arbitrary character animation approaches using a common interface.
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    Presenting a Deep Motion Blending Approach for Simulating Natural Reach Motions
    (The Eurographics Association, 2018) Gaisbauer, Felix; Froehlich, Philipp; Lehwald, Jannes; Agethen, Philipp; Rukzio, Enrico; Jain, Eakta and Kosinka, Jirí
    Motion 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.