EG 2018 - Posters

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Browsing EG 2018 - Posters by Subject "Motion capture"

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    A Probabilistic Motion Planning Algorithm for Realistic Walk Path Simulation
    (The Eurographics Association, 2018) Agethen, Philipp; Neher, Thomas; Gaisbauer, Felix; Manns, Martin; Rukzio, Enrico; Jain, Eakta and Kosinka, Jirí
    This paper presents an approach that combines a hybrid A* path planner with a statistical motion graph to effectively generate a rich repertoire of walking trajectories. The motion graph is generated from a comprehensive database (20 000 steps) of captured human motion and covers a wide range of gait variants. The hybrid A* path planner can be regarded as an orchestrationinstance, stitching together succeeding left and right steps, which were drawn from the statistical motion model. Moreover, the hybrid A* planner ensures a collision-free path between a start and an end point. A preliminary evaluation underlines the evident benefits of the proposed algorithm.
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    Smoothing Noisy Skeleton Data in Real Time
    (The Eurographics Association, 2018) Hoxey, Thomas; Stephenson, Ian; Jain, Eakta and Kosinka, Jirí
    The aim of this project is to be able to visualise live skeleton tracking data in a virtual analogue of a real world environment, to be viewed in VR. Using a single RGBD camera motion tracking method is a cost effective way to get real time 3D skeleton tracking data. Not only this but people being tracked don't need any special markers. This makes it much more practical for use in a non studio or lab environment. However the skeleton it provides is not as accurate as a traditional multiple camera system. With a single fixed view point the body can easily occlude itself, for example by standing side on to the camera. Secondly without marked tracking points there can be inconsistencies with where the joints are identified, leading to inconsistent body proportions. In this paper we outline a method for improving the quality of motion capture data in real time, providing an off the shelf framework for importing the data into a virtual scene. Our method uses a two stage approach to smooth smaller inconsistencies and try to estimate the position of improperly proportioned or occluded joints.