Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 53-56.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, such as blending parameters, needed in
the second stage. In the second stage, we synthesize a new human motion in realtime,
depending on the blending techniques selected. Three different blending
techniques, namely linear blending, cubic blending and anticipation-based blending,
are provided to the 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.Dirican, HüseyinM.S
