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• W2810607340 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." @default.
• W2810607340 created "2018-07-10" @default.
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• W2810607340 date "2018-01-01" @default.
• W2810607340 modified "2023-09-30" @default.
• W2810607340 title "Deep Autoencoder for Combined Human Pose Estimation and Body Model Upscaling" @default.
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• W2810607340 doi "https://doi.org/10.1007/978-3-030-01249-6_48" @default.
• W2810607340 hasPublicationYear "2018" @default.
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