Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

ECCV 2018 Helge RhodinMathieu SalzmannPascal Fua

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... (read more)

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