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We pose constraint-based motion synthesis as a\n            <jats:italic>maximum a posterior<\/jats:italic>\n            (MAP) problem and develop an optimization framework that generates natural motion satisfying user constraints. The system automatically learns a statistical dynamic model from motion capture data and then enforces it as a motion prior. This motion prior, together with user-defined constraints, comprises a trajectory optimization problem. Solving this problem in the low-dimensional space yields optimal natural motion that achieves the goals specified by the user. We demonstrate the effectiveness of this approach by generating whole-body and facial motion from a variety of spatial-temporal constraints.\n          <\/jats:p>","DOI":"10.1145\/1276377.1276387","type":"journal-article","created":{"date-parts":[[2007,9,14]],"date-time":"2007-09-14T13:44:55Z","timestamp":1189777495000},"page":"8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["Constraint-based motion optimization using a statistical dynamic model"],"prefix":"10.1145","volume":"26","author":[{"given":"Jinxiang","family":"Chai","sequence":"first","affiliation":[{"name":"Texas A&amp;M University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jessica K.","family":"Hodgins","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2007,7,29]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1028523.1028546"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566606"},{"key":"e_1_2_2_3_1","volume-title":"Nonl-inear Programming: Theory and Algorithms","author":"Bazaraa M. 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