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arXiv · 1807.01511

Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling

Matthew Trumble·Andrew Gilbert·Adrian Hilton·John Collomosse
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Abstract

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.

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Matthew Trumble, Andrew Gilbert, Adrian Hilton, John Collomosse. 2018-07-04. Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling. https://arxiv.org/abs/1807.01511

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