{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T18:52:24Z","timestamp":1793127144804,"version":"4.5.0"},"reference-count":26,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2009,12,1]],"date-time":"2009-12-01T00:00:00Z","timestamp":1259625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2009,12]]},"abstract":"<jats:p>\n            We present a novel method to model and synthesize variation in motion data. Given a few examples of a particular type of motion as input, we learn a generative model that is able to synthesize a family of spatial and temporal variants that are statistically similar to the input examples. The new variants retain the features of the original examples, but are\n            <jats:italic>not exact copies<\/jats:italic>\n            of them. We learn a Dynamic Bayesian Network model from the input examples that enables us to capture properties of conditional independence in the data, and model it using a multivariate probability distribution. We present results for a variety of human motion, and 2D handwritten characters. We perform a user study to show that our new variants are less repetitive than typical game and crowd simulation approaches of re-playing a small number of existing motion clips. Our technique can synthesize new variants efficiently and has a small memory requirement.\n          <\/jats:p>","DOI":"10.1145\/1618452.1618517","type":"journal-article","created":{"date-parts":[[2009,12,8]],"date-time":"2009-12-08T20:53:14Z","timestamp":1260305594000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":57,"title":["Modeling spatial and temporal variation in motion data"],"prefix":"10.1145","volume":"28","author":[{"given":"Manfred","family":"Lau","sequence":"first","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziv","family":"Bar-Joseph","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Kuffner","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2009,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Bodenheimer B. Shleyfman A. V. and Hodgins J. K. 1999. The effects of noise on the perception of animated human running. In Computer Animation and Simulation 1999 N. Magnenat-Thalmann and D. Thalmann Eds. 53--63.  Bodenheimer B. Shleyfman A. V. and Hodgins J. K. 1999. The effects of noise on the perception of animated human running. In Computer Animation and Simulation 1999 N. Magnenat-Thalmann and D. Thalmann Eds. 53--63.","DOI":"10.1007\/978-3-7091-6423-5_6"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344865"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276387"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/344779.344882"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/msb4100115"},{"key":"e_1_2_1_6_1","unstructured":"Friedman N. Murphy K. and Russell S. 1998. Learning the structure of dynamic probabilistic networks. Uncertainty in Artifical Intelligence 139--147.  Friedman N. Murphy K. and Russell S. 1998. Learning the structure of dynamic probabilistic networks. Uncertainty in Artifical Intelligence 139--147."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Ghahramani Z. 1998. Learning dynamic bayesian networks. C. L. Giles and M. Gori (eds.) Adaptive Processing of Sequences and Data Structures. Lecture Notes in Artificial Intelligence 168--197.   Ghahramani Z. 1998. Learning dynamic bayesian networks. C. L. Giles and M. Gori (eds.) Adaptive Processing of Sequences and Data Structures. 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E. 2003. Learning Bayesian Networks. Prentice Hall.   Neapolitan R. E. 2003. Learning Bayesian Networks . Prentice Hall."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/2945.468392"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of Computer Animation, IEEE Computer Society, 36--42","author":"Pullen K."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566608"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/38.708559"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1103900.1103934"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1167"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/38.626968"},{"key":"e_1_2_1_26_1","volume-title":"ACM SIGGRAPH\/Eurographics Symposium on Computer Animation (SCA)","author":"Yu Q.","year":"2007"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1618452.1618517","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1618452.1618517","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T12:23:23Z","timestamp":1750249403000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1618452.1618517"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,12]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2009,12]]}},"alternative-id":["10.1145\/1618452.1618517"],"URL":"https:\/\/doi.org\/10.1145\/1618452.1618517","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2009,12]]},"assertion":[{"value":"2009-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}