Centralized Networks to Generate Human Body Motions

Sergey Vakulenko, Ovidiu Radulescu, Ivan Morozov, and Andreas Weber
In: Sensors (2017), 17:12(2907)
 

Abstract

We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons’ states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers’ trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings. Keywords:

Keywords: human body motions, markers, motion reconstruction 1., motion representation, motion sensors, neural networks

Bibtex

@ARTICLE{VakulenkoRadulescuMorozovWeber2017,
    author = {Vakulenko, Sergey and Radulescu, Ovidiu and Morozov, Ivan and Weber, Andreas},
     pages = {2907},
     title = {Centralized Networks to Generate Human Body Motions},
   journal = {Sensors},
    volume = {17},
    number = {12},
      year = {2017},
  keywords = {human body motions, markers, motion reconstruction 1., motion representation, motion sensors, neural
              networks},
  abstract = {We consider continuous-time recurrent neural networks as dynamical models for the simulation of
              human body motions. These networks consist of a few centers and many satellites connected to them.
              The centers evolve in time as periodical oscillators with different frequencies. The center states
              define the satellite neurons’ states by a radial basis function (RBF) network. To simulate
              different motions, we adjust the parameters of the RBF networks. Our network includes a switching
              module that allows for turning from one motion to another. Simulations show that this model allows
              us to simulate complicated motions consisting of many different dynamical primitives. We also use
              the model for learning human body motion from markers’ trajectories. We find that center
              frequencies can be learned from a small number of markers and can be transferred to other markers,
              such that our technique seems to be capable of correcting for missing information resulting from
              sparse control marker settings.
              Keywords:},
       url = {http://www.mdpi.com/1424-8220/17/12/2907},
       doi = {10.3390/s17122907}
}