Data-driven Motion Estimation with Low-Cost Sensors

Motion capture can produce high quality data for motion generation. However, that 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 captured by the professional motion capture systems. We employ eight 3D accelerometers — four Nintendo c © Wii controllers — attached to a performer’s body to capture motion data. The collected data 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.