Data-driven motion estimation with low-cost sensors - Texas A&M University (TAMU) Scholar

Motion capture can produce high quality data for motion generation. However, professional motion capture is expensive, and imposes restrictions on the capturing environment. We propose a motion estimation framework that utilizes a small set of low-cost, 3D acceleration sensors. We use a data-driven approach to synthesize realistic human motion comparable in quality to the motion generated by the professional motion capture systems. We employ eight 3D accelerometers - four Nintendo Wii controllers - attached to a performer's body as the input signal, which is used to synthesize high quality motion from a statistical model learned from a high quality motion capture database. The proposed system is inexpensive and is easy to setup. 2008 The Institution of Engineering and Technology.

Data-driven motion estimation with low-cost sensors Conference Paper