{"title":"面部肌肉激活的动作捕捉","authors":"Eftychios Sifakis, Ronald Fedkiw","doi":"10.1109/CVPR.2005.154","DOIUrl":null,"url":null,"abstract":"Biomechanically accurate finite element models of facial musculature offer a superior accuracy in reproducing facial expressions. We employ such a finite element simulation model to determine the muscle activations and kinematic configuration of the rigid bones associated with an expression from a sparse sampling of the deformation of the face surface over time, acquired using a motion capture system. Our simulation model, consisting of 840K tetrahedral elements, was created through non-rigid registration of a muscle geometry template derived from the visible human dataset to MRI volumetric data acquired from the motion capture subject.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"82 1","pages":"1195"},"PeriodicalIF":0.0000,"publicationDate":"2005-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Facial Muscle Activations from Motion Capture\",\"authors\":\"Eftychios Sifakis, Ronald Fedkiw\",\"doi\":\"10.1109/CVPR.2005.154\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Biomechanically accurate finite element models of facial musculature offer a superior accuracy in reproducing facial expressions. We employ such a finite element simulation model to determine the muscle activations and kinematic configuration of the rigid bones associated with an expression from a sparse sampling of the deformation of the face surface over time, acquired using a motion capture system. Our simulation model, consisting of 840K tetrahedral elements, was created through non-rigid registration of a muscle geometry template derived from the visible human dataset to MRI volumetric data acquired from the motion capture subject.\",\"PeriodicalId\":89346,\"journal\":{\"name\":\"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops\",\"volume\":\"82 1\",\"pages\":\"1195\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CVPR.2005.154\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2005.154","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Biomechanically accurate finite element models of facial musculature offer a superior accuracy in reproducing facial expressions. We employ such a finite element simulation model to determine the muscle activations and kinematic configuration of the rigid bones associated with an expression from a sparse sampling of the deformation of the face surface over time, acquired using a motion capture system. Our simulation model, consisting of 840K tetrahedral elements, was created through non-rigid registration of a muscle geometry template derived from the visible human dataset to MRI volumetric data acquired from the motion capture subject.