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Unsupervised Learning on Monocular Videos for 3D Human Pose Estimation

2020-12-02 20:27:35
Sina Honari, Victor Constantin, Helge Rhodin, Mathieu Salzmann, Pascal Fua
arXiv_CV
             
Abstract

In this paper, we introduce an unsupervised feature extraction method that exploits contrastive self-supervised (CSS) learning to extract rich latent vectors from single-view videos. 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 separate each latent vector into a time-variant component and a time-invariant one. We then show that applying CSS only to the time-variant features, while also reconstructing the input and encouraging a gradual transition between nearby and away features yields a rich latent space, well-suited for human pose estimation. Our approach outperforms other unsupervised single-view methods and match the performance of multi-view techniques.

Abstract (translated)

URL

https://arxiv.org/abs/2012.01511

PDF

https://arxiv.org/pdf/2012.01511.pdf

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