Song, D., Tong, R., Chang, J., Yang, X., Tang, M. and Zhang, J. J., 2016. 3D Body Shapes Estimation from Dressed-Human Silhouettes. Computer Graphics Forum, 35 (7), 147 - 156.
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Estimation of 3D body shapes from dressed-human photos is an important but challenging problem in virtual fitting. We propose a novel automatic framework to efficiently estimate 3D body shapes under clothes. We construct a database of 3D naked and dressed body pairs, based on which we learn how to predict 3D positions of body landmarks (which further constrain a parametric human body model) automatically according to dressed-human silhouettes. Critical vertices are selected on 3D registered human bodies as landmarks to represent body shapes, so as to avoid the time-consuming vertices correspondences finding process for parametric body reconstruction. Our method can estimate 3D body shapes from dressed-human silhouettes within 4 seconds, while the fastest method reported previously need 1 minute. In addition, our estimation error is within the size tolerance for clothing industry. We dress 6042 naked bodies with 3 sets of common clothes by physically based cloth simulation technique. To the best of our knowledge, We are the first to construct such a database containing 3D naked and dressed body pairs and our database may contribute to the areas of human body shapes estimation and cloth simulation.
|Additional Information:||This is the peer reviewed version of the following article: [Song, D., Tong, R., Chang, J., Yang, X., Tang, M. and Zhang, J. J., 2016. 3D Body Shapes Estimation from Dressed-Human Silhouettes. Computer Graphics Forum, 35 (7), 147 - 156] which has been published in final form at DOI: 10.1111/cgf.13012. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.|
|Uncontrolled Keywords:||Image-based modeling|
|Group:||Faculty of Media & Communication|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||17 Nov 2016 14:54|
|Last Modified:||17 Nov 2016 14:54|
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