TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	REMOVE
3D Human Pose Estimation	Total Capture	AutoEnc	Average MPJPE (mm)	35	# 9

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Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling

ECCV 2018 · Matthew Trumble, Andrew Gilbert, Adrian Hilton, John Collomosse ·

We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of $4 \times$, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has the potential for passive human behaviour monitoring where there is a requirement for high fidelity estimation of human body shape and pose.