Learning from Synthetic Humans

CVPR 2017 Gül VarolJavier RomeroXavier MartinNaureen MahmoodMichael J. BlackIvan LaptevCordelia Schmid

Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs)... (read more)

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