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• W2965207611 abstract "In this paper, we address the problem of learning 3D human pose and body shape from 2D image dataset, without having to use 3D dataset (body shape and pose). The idea is to use dense correspondences between image points and a body surface, which can be annotated on in-the wild 2D images, and extract and aggregate 3D information from them. To do so, we propose a training strategy called ``deform-and-learn where we alternate deformable surface registration and training of deep convolutional neural networks (ConvNets). Unlike previous approaches, our method does not require 3D pose annotations from a motion capture (MoCap) system or human intervention to validate 3D pose annotations." @default.
• W2965207611 created "2019-08-13" @default.
• W2965207611 creator A5023347873 @default.
• W2965207611 creator A5074476931 @default.
• W2965207611 date "2019-07-27" @default.
• W2965207611 modified "2023-09-24" @default.
• W2965207611 title "Learning Body Shape and Pose from Dense Correspondences" @default.
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• W2965207611 hasPublicationYear "2019" @default.
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