{"title":"基于逆图形的无监督姿态估计","authors":"J. Jennings, F. Kamangar","doi":"10.1109/CSCI49370.2019.00136","DOIUrl":null,"url":null,"abstract":"We introduce a method for pose estimation from sensor array data utilizing an inverse graphics approach. We propose a general unsupervised method based on manipulating a 3D model to discover the joint-space parameters that produce an observed image or sensor reading. Using our novel architecture, we discuss the design of a model for performing human pose estimation on data obtained from a mattress pressure sensor.","PeriodicalId":103662,"journal":{"name":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"149 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unsupervised Pose Estimation via Inverse Graphics\",\"authors\":\"J. Jennings, F. Kamangar\",\"doi\":\"10.1109/CSCI49370.2019.00136\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We introduce a method for pose estimation from sensor array data utilizing an inverse graphics approach. We propose a general unsupervised method based on manipulating a 3D model to discover the joint-space parameters that produce an observed image or sensor reading. Using our novel architecture, we discuss the design of a model for performing human pose estimation on data obtained from a mattress pressure sensor.\",\"PeriodicalId\":103662,\"journal\":{\"name\":\"2019 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"volume\":\"149 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCI49370.2019.00136\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI49370.2019.00136","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We introduce a method for pose estimation from sensor array data utilizing an inverse graphics approach. We propose a general unsupervised method based on manipulating a 3D model to discover the joint-space parameters that produce an observed image or sensor reading. Using our novel architecture, we discuss the design of a model for performing human pose estimation on data obtained from a mattress pressure sensor.
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