T. Edmunds, S. Muthukrishnan, Subarna Sadhukhan, S. Sueda
{"title":"MoDB: database system for synthesizing human motion","authors":"T. Edmunds, S. Muthukrishnan, Subarna Sadhukhan, S. Sueda","doi":"10.1109/ICDE.2005.89","DOIUrl":null,"url":null,"abstract":"Enacting and capturing real motion for all potential scenarios is prohibitively expensive; hence, there is a great demand to synthetically generate realistic human motion. However, it is a central challenge in character animation to synthetically generate a large sequence of smooth human motion. We present a novel, database-centric solution to address this challenge. We demonstrate a method of generating long sequences of motion by performing various similarity-based \"joins\" on a database of captured motion sequences. This article illustrates our system (MoDB) and showcases the process of encoding captured motion into relational data and generating realistic motion by concatenating sub-sequences of the captured data according to feasibility metrics. The demo features an interactive character that moves towards user-specified targets; the character 's motion is generated by relying on the real time performance of the database for indexing and selection of feasible sub-sequences.","PeriodicalId":297231,"journal":{"name":"21st International Conference on Data Engineering (ICDE'05)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"21st International Conference on Data Engineering (ICDE'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2005.89","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Enacting and capturing real motion for all potential scenarios is prohibitively expensive; hence, there is a great demand to synthetically generate realistic human motion. However, it is a central challenge in character animation to synthetically generate a large sequence of smooth human motion. We present a novel, database-centric solution to address this challenge. We demonstrate a method of generating long sequences of motion by performing various similarity-based "joins" on a database of captured motion sequences. This article illustrates our system (MoDB) and showcases the process of encoding captured motion into relational data and generating realistic motion by concatenating sub-sequences of the captured data according to feasibility metrics. The demo features an interactive character that moves towards user-specified targets; the character 's motion is generated by relying on the real time performance of the database for indexing and selection of feasible sub-sequences.