Natural Posture Blending Using Deep Neural Networks

Felix Gaisbauer, Jannes Lehwald, Janis Sprenger, Enrico Rukzio

In: Motion, Interaction and Games. ACM SIGGRAPH Conference on Motion in Games (MIG-2019) October 28-30 Newcastle Upon Tyne United Kingdom Seiten 2-1 MIG '19 ISBN 978-1-4503-6994-7 ACM 10/2019.


Motion synthesis approaches are widely used throughout different domains such as gaming, virtual crowds or simulation within production industries. With ongoing digitization, these systems are becoming increasingly indispensable. In general, the utilized technologies can be subdivided in data-driven and model-based approaches, whereas each category has its advantages and disadvantages. In the field of data-driven motion synthesis, recent works present deep learning based approaches for full body 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 for blending collision-free and feasible postures between a humanoid start and target posture. The network has been trained utilizing the CMU database to generate feasible postures. The proposed approach can be utilized for posture-blending, motion synthesis with known start and end-posture or key-frame animation. A preliminary evaluation indicates the validity and the potential of the novel approach.


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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence