Unsupervised Temporal Learning on Monocular Videos for 3D Human Pose Estimation. (arXiv:2012.01511v3 [cs.CV] UPDATED)

In this paper we propose an unsupervised learning method to extract temporal
information on monocular videos, where we detect and encode subject of interest
in each frame and leverage contrastive self-supervised (CSS) learning to
extract rich latent vectors. Instead of simply treating the latent features of
nearby frames as positive pairs and those of temporally-distant ones as
negative pairs as in other CSS approaches, we explicitly disentangle each
latent vector into a time-variant component and a time-invariant one. We then …

3d arxiv cv human learning unsupervised videos