745 research outputs found
Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach
The ability to sense, localize, and estimate the 3D position and orientation
of the human body is critical in virtual reality (VR) and extended reality (XR)
applications. This becomes more important and challenging with the deployment
of VR/XR applications over the next generation of wireless systems such as 5G
and beyond. In this paper, we propose a novel framework that can reconstruct
the 3D human body pose of the user given sparse measurements from Inertial
Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically,
our framework enables reliable transmission of compressed IMU signals through
noisy wireless channels and effective recovery of such signals at the receiver,
e.g., an edge server. This task is very challenging due to the constraints of
transmit power, recovery accuracy, and recovery latency. To address these
challenges, we first develop a deep generative model at the receiver to recover
the data from linear measurements of IMU signals. The linear measurements of
the IMU signals are obtained by a linear projection with a measurement matrix
based on the compressive sensing theory. The key to the success of our
framework lies in the novel design of the measurement matrix at the
transmitter, which can not only satisfy power constraints for the IMU devices
but also obtain a highly accurate recovery for the IMU signals at the receiver.
This can be achieved by extending the set-restricted eigenvalue condition of
the measurement matrix and combining it with an upper bound for the power
transmission constraint. Our framework can achieve robust performance for
recovering 3D human poses from noisy compressed IMU signals. Additionally, our
pre-trained deep generative model achieves signal reconstruction accuracy
comparable to an optimization-based approach, i.e., Lasso, but is an order of
magnitude faster
An introduction to continuous optimization for imaging
International audienceA large number of imaging problems reduce to the optimization of a cost function , with typical structural properties. The aim of this paper is to describe the state of the art in continuous optimization methods for such problems, and present the most successful approaches and their interconnections. We place particular emphasis on optimal first-order schemes that can deal with typical non-smooth and large-scale objective functions used in imaging problems. We illustrate and compare the different algorithms using classical non-smooth problems in imaging, such as denoising and deblurring. Moreover, we present applications of the algorithms to more advanced problems, such as magnetic resonance imaging, multilabel image segmentation, optical flow estimation, stereo matching, and classification
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