2,063 research outputs found
Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling
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 , 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
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
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