Performance-based control interfaces using mixture of factor analyzers.
ABSTRACT This paper introduces an approach to performance animation that employs a small number of inertial measurement sensors to
create an easy-to-use system for an interactive control of a full-body human character. Our key idea is to construct a global
model from a prerecorded motion database and utilize them to construct full-body human motion in a maximum a posteriori framework
(MAP). We have demonstrated the effectiveness of our system by controlling a variety of human actions, such as boxing, golf
swinging, and table tennis, in real time. One unique property of our system is its ability to learn priors from a large and
heterogeneous motion capture database and use them to generate a wide range of natural poses, a capacity that has not been
demonstrated in previous data-driven character posing systems.
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ABSTRACT: Our study proposes a new local model to accurately control an avatar using six inertial sensors in real-time. Creating such a system to assist interactive control of a full-body avatar is challenging because control signals from our performance interfaces are usually inadequate to completely determine the whole body movement of human actors. We use a pre-captured motion database to construct a group of local regression models, which are used along with the control signals to synthesize whole body human movement. By synthesizing a variety of human movements based on actors’ control in real-time, this study verifies the effectiveness of the proposed system. Compared with the previous models, our proposed model can synthesize more accurate results. Our system is suitable for common use because it is much cheaper than commercial motion capture systems.Sciece China. Information Sciences · 0.71 Impact Factor