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×, 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 behavior monitoring where there is a requirement for high fidelity estimation of human body shape and pose. Keywords: Deep Learning, Pose Estimation, Multiple Viewpoint Video Fig. 1 . Simultaneous estimation of 3D human pose and 4× upscaled volumetric body shape, from coarse visual hull data derived from a sparse set of wide-baseline views.
Trumble et al. REF introduce a convolutional symmetric autoencoder with reconstruction loss to encode skeletal joint positions, which simultaneously learns a deep representation of volumetric body shape.
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
ArXiv
ArXiv
Computer Science