  In this paper we propose an unsupervised feature extraction method to capture
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
show that applying contrastive loss only to the time-variant features and
encouraging a gradual transition on them between nearby and away frames while
also reconstructing the input, extract rich temporal features, well-suited for
human pose estimation. Our approach reduces error by about 50% compared to the
standard CSS strategies, outperforms other unsupervised single-view methods and
matches the performance of multi-view techniques. When 2D pose is available,
our approach can extract even richer latent features and improve the 3D pose
estimation accuracy, outperforming other state-of-the-art weakly supervised
methods.
