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We address the problem of estimating human pose and body shape from 3D scansover time. Reliable estimation of 3D body shape is necessary for manyapplications including virtual try-on, health monitoring, and avatar creationfor virtual reality. Scanning bodies in minimal clothing, however, presents apractical barrier to these applications. We address this problem by estimatingbody shape under clothing from a sequence of 3D scans. Previous methods thathave exploited body models produce smooth shapes lacking personalized details.We contribute a new approach to recover a personalized shape of the person. Theestimated shape deviates from a parametric model to fit the 3D scans. Wedemonstrate the method using high quality 4D data as well as sequences ofvisual hulls extracted from multi-view images. We also make available BUFF, anew 4D dataset that enables quantitative evaluation(http://buff.is.tue.mpg.de). Our method outperforms the state of the art inboth pose estimation and shape estimation, qualitatively and quantitatively.

Detailed, accurate, human shape estimation from clothed 3D scan sequences