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Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation
[article]
2020
arXiv
pre-print
The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire. To reduce this annotation dependency, we propose Multiview-Consistent Semi Supervised Learning (MCSS) framework that utilizes similarity in pose information from unannotated, uncalibrated but synchronized multi-view videos of human motions as additional weak supervision
arXiv:1908.05293v3
fatcat:35thk5rmcjfwtbfads7ej5mmty