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.

翻译：在本文中,我们提出了人类身体重建中第一种未经监督的深层方法,用稀少的地标(叫做DeepMurf)来估计人体表面。我们用一个自定义的自动编码器来估计缺失的地标。然后我们用一个关注模型来估计地标的人体交点。最后,用一个递归网络来测重塑人体的统计基因模型的回归参数。我们提出的一套损失功能使我们能够以不受监督的方式对网络进行培训。四个公共数据集的结果显示,我们的方法准确地将人体从真实的世界的机能数据中重建出来。