Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

Helge Rhodin; Mathieu Salzmann; Pascal Fua

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arXiv · 1804.01110

Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

Helge Rhodin·Mathieu Salzmann·Pascal Fua

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Abstract

Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a sufficiently large set of samples with 3D annotations for learning to succeed. In this paper, we propose to overcome this problem by learning a geometry-aware body representation from multi-view images without annotations. To this end, we use an encoder-decoder that predicts an image from one viewpoint given an image from another viewpoint. Because this representation encodes 3D geometry, using it in a semi-supervised setting makes it easier to learn a mapping from it to 3D human pose. As evidenced by our experiments, our approach significantly outperforms fully-supervised methods given the same amount of labeled data, and improves over other semi-supervised methods while using as little as 1% of the labeled data.

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BibTeX RIS

Helge Rhodin, Mathieu Salzmann, Pascal Fua. 2018-04-03. Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation. https://arxiv.org/abs/1804.01110

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