Department of Computer Engineering
MS Thesis Presentation
Data-Driven Synthesis of Realistic Human Motion Using Motion Graphs
Computer Engineering Department
Realistic human motions is an essential part of diverse range of media, such as feature films, video games and virtual environments. Motion capture provides realistic human motion data using sensor technology.
However, motion capture data is not flexible. This drawback limits the utility of motion capture in practice. In this thesis, we propose a two-stage approach that makes the motion captured data reusable to synthesize new motions in real-time via motion graphs. Starting from a dataset of various motions, we construct a motion graph of similar motion segments and calculate the parameters needed in the second stage such as blending parameters. In the second stage, we synthesize new human motion in real-time, depending on the blending techniques selected. Three different blending techniques, namely linear blending, cubic blending and prepared blending, are provided to user. In addition, motion clip preference approach, which is applied to the motion search algorithm, enable users to control the motion clip types in the result motion.
DATE: 03 July, 2014, Thursday @ 09:30