Indirect deep structured learning for 3D human body shape and pose prediction

© 2017. The copyright of this document resides with its authors. In this paper we present a novel method for 3D human body shape and pose prediction. Our work is motivated by the need to reduce our reliance on costly-to-obtain ground truth labels. To achieve this, we propose training an encoder-decoder network using a two step procedure as follows. During the first step, a decoder is trained to predict a body silhouette using SMPL [2] (a statistical body shape model) parameters as an input. During the second step, the whole network is trained on real image and corresponding silhouette pairs while the decoder is kept fixed. Such a procedure allows for an indirect learning of body shape and pose parameters from real images without requiring any ground truth parameter data. Our key contributions include: (a) a novel encoder-decoder architecture for 3D body shape and pose prediction, (b) corresponding training procedure as well as (c) quantitative and qualitative analysis of the proposed method on artificial and real image datasets