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• W2892270791 abstract "In monocular 3D human pose estimation a common setup is to first detect 2D positions and then lift the detection into 3D coordinates. Many algorithms suffer from overfitting to camera positions in the training set. We propose a siamese architecture that learns a rotation equivariant hidden representation to reduce the need for data augmentation. Our method is evaluated on multiple databases with different base networks and shows a consistent improvement of error metrics. It achieves state-of-the-art cross-camera error rate among algorithms that use estimated 2D joint coordinates only." @default.
• W2892270791 created "2018-09-27" @default.
• W2892270791 creator A5055832443 @default.
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• W2892270791 date "2019-04-01" @default.
• W2892270791 modified "2023-10-18" @default.
• W2892270791 title "3D human pose estimation with siamese equivariant embedding" @default.
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• W2892270791 doi "https://doi.org/10.1016/j.neucom.2019.02.029" @default.
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