  In this paper we propose the first deep unsupervised approach in human body
reconstruction to estimate body surface from a sparse set of landmarks, so
called DeepMurf. We apply a denoising autoencoder to estimate missing
landmarks. Then we apply an attention model to estimate body joints from
landmarks. Finally, a cascading network is applied to regress parameters of a
statistical generative model that reconstructs body. Our set of proposed loss
functions allows us to train the network in an unsupervised way. Results on
four public datasets show that our approach accurately reconstructs the human
body from real world mocap data.
