Combining Sampling and Autoregression for Motion Synthesis

DJ Oziem, NW Campbell, CJ Dalton, DP Gibson, BT Thomas

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)


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.
Translated title of the contributionCombining Sampling and Autoregression for Motion Synthesis
Original languageEnglish
Pages (from-to)510 - 513
Number of pages4
JournalProceedings of the Computer Graphics International (CGI'04)
Publication statusPublished - 6 Jul 2004

Bibliographical note

ISBN: 0769521711
Publisher: IEEE Computer Society
Name and Venue of Conference: 21st Computer Graphics International Conference (CGI 04), Heraklion, Crete, 16-19 June


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