Combining Sampling and Autoregression for Motion Synthesis

We present a novel approach to motion synthesis. We show that by splitting sequences into segments we can create new sequences with a similar look and feel to the original. Copying segments of the original data generates a sequence which maintains detailed characteristics. By modelling each segment using an autoregressive process we can introduce new segments and therefore unseen motions. These statistical models allow a potentially infinite number of new segments to be generated. We show that this system can model complicated nonstationary sequences which a single ARP is unable to do.