Generating Motion Trajectories by Sparse Activation of Learned Motion Primitives

We interpret biological motion trajectories as composed of sequences of sub-blocks or motion primitives. Such primitives, together with the information, when they occur during a motion, provide a compact representation of movement. We present a two-layer model for movement generation, where the higher level consists of a number of spiking neurons that trigger motion primitives in the lower level. Given a set of handwritten character trajectories, we learn motion primitives, together with the timing information, with a variant of shift-NMF that is able to cope with large data sets. From the timing information for a class of characters, we then learn a generative model based on a stochastic Integrate-and-Fire neuron model. We show that we can generate good reconstructions of characters with shared primitives for all characters modeled.