Spike Train Driven Dynamical Models for Human Actions


We investigate dynamical models of human motion that can support both synthesis and analysis tasks. We seek models that have fine-scale representational power and can therefore model subtle differences in the way an action is performed. To this end, we model an observed action as an (unknown) linear time-invariant dynamical model of relatively small order, driven by a sparse bounded input signal.

Our motivating intuition is that the time-invariant dynamics will capture the unchanging physical characteristics of an actor, while the inputs used to excite the system will correspond to a causal signature of the action being performed.

The Model
Given a time series data (e.g. motion capture data):

  1. Estimate the state of the model
  2. Identify the model parameters
  3. Estimate the input that drives the system

Reconstruction Example