  Learning a good 3D human pose representation is important for human pose
related tasks, e.g. human 3D pose estimation and action recognition. Within all
these problems, preserving the intrinsic pose information and adapting to view
variations are two critical issues. In this work, we propose a novel Siamese
denoising autoencoder to learn a 3D pose representation by disentangling the
pose-dependent and view-dependent feature from the human skeleton data, in a
fully unsupervised manner. These two disentangled features are utilized
together as the representation of the 3D pose. To consider both the kinematic
and geometric dependencies, a sequential bidirectional recursive network
(SeBiReNet) is further proposed to model the human skeleton data. Extensive
experiments demonstrate that the learned representation 1) preserves the
intrinsic information of human pose, 2) shows good transferability across
datasets and tasks. Notably, our approach achieves state-of-the-art performance
on two inherently different tasks: pose denoising and unsupervised action
recognition. Code and models are available at:
\url{https://github.com/NIEQiang001/unsupervised-human-pose.git}
