{"title":"Talking heads: introducing the tool of 3D motion fields in the study of action","authors":"J. Neumann, Y. Aloimonos","doi":"10.1109/HUMO.2000.897367","DOIUrl":"https://doi.org/10.1109/HUMO.2000.897367","url":null,"abstract":"We demonstrate a method to complete three-dimensional (3D) motion fields on a face to serve as an intermediate representation for the study of actions. Twelve synchronized and calibrated cameras are positioned all around a talking person and observe its head in motion. We represent the head as a deformable mesh, which is fitted in a global optimization step to silhouette-contour and multi-camera stereo data derived from all images. The non-rigid displacement of the mesh from frame to frame, the 3D motion field, is determined from the normal flow information in all the images. We integrate these cues over time, thus producing a spatio-temporal representation of the talking head. Our ability to estimate 3D motion fields points to a new framework for the study of action. Using multicamera configurations we can estimate a sequence of evolving 3D motion fields representing specific actions. Then, by performing a geometric and statistical analysis on these structures, we can achieve dimensionality reduction and thus come up with powerful representations of generic human action.","PeriodicalId":384462,"journal":{"name":"Proceedings Workshop on Human Motion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Specialized mappings and the estimation of human body pose from a single image","authors":"Rómer Rosales, S. Sclaroff","doi":"10.1109/HUMO.2000.897366","DOIUrl":"https://doi.org/10.1109/HUMO.2000.897366","url":null,"abstract":"We present an approach for recovering articulated body pose from single monocular images using the Specialized Mappings Architecture (SMA), a nonlinear supervised learning architecture. SMAs consist of several specialized forward (input to output space) mapping functions and a feedback matching function, estimated automatically from data. Each of these forward functions maps certain areas (possibly disconnected) of the input space onto the output space. A probabilistic model for the architecture is first formalized along with a mechanism for learning its parameters. The learning problem is approached using a maximum likelihood estimation framework; we present expectation maximization (EM) algorithms for several different choices of the likelihood function. The performance of the presented solutions under these different likelihood functions is compared in the task of estimating human body posture from low-level visual features obtained from a single image, showing promising results.","PeriodicalId":384462,"journal":{"name":"Proceedings Workshop on Human Motion","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129609300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}