A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing

• URL: http://arxiv.org/abs/2409.00087v1
• Date: Mon, 26 Aug 2024 02:57:23 GMT
• Title: A Lightweight Human Pose Estimation Approach for Edge Computing-Enabled Metaverse with Compressive Sensing
• Authors: Nguyen Quang Hieu, Dinh Thai Hoang, Diep N. Nguyen,
• Abstract summary: The ability to estimate 3D movements of users over edge computing-enabled networks, such as 5G/6G networks, is a key enabler for the new era of extended reality (XR) and Metaverse applications. Recent advancements in deep learning have shown advantages over optimization techniques for estimating 3D human poses given spare measurements from sensor signals. We propose a novel approach for redundancy removal and lightweight transmission of IMU signals over noisy wireless environments.
• Score: 23.884862152830184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ability to estimate 3D movements of users over edge computing-enabled networks, such as 5G/6G networks, is a key enabler for the new era of extended reality (XR) and Metaverse applications. Recent advancements in deep learning have shown advantages over optimization techniques for estimating 3D human poses given spare measurements from sensor signals, i.e., inertial measurement unit (IMU) sensors attached to the XR devices. However, the existing works lack applicability to wireless systems, where transmitting the IMU signals over noisy wireless networks poses significant challenges. Furthermore, the potential redundancy of the IMU signals has not been considered, resulting in highly redundant transmissions. In this work, we propose a novel approach for redundancy removal and lightweight transmission of IMU signals over noisy wireless environments. Our approach utilizes a random Gaussian matrix to transform the original signal into a lower-dimensional space. By leveraging the compressive sensing theory, we have proved that the designed Gaussian matrix can project the signal into a lower-dimensional space and preserve the Set-Restricted Eigenvalue condition, subject to a power transmission constraint. Furthermore, we develop a deep generative model at the receiver to recover the original IMU signals from noisy compressed data, thus enabling the creation of 3D human body movements at the receiver for XR and Metaverse applications. Simulation results on a real-world IMU dataset show that our framework can achieve highly accurate 3D human poses of the user using only $82\%$ of the measurements from the original signals. This is comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster.

Related papers

• DM3D: Distortion-Minimized Weight Pruning for Lossless 3D Object Detection [42.07920565812081]
  We propose a novel post-training weight pruning scheme for 3D object detection. It determines redundant parameters in the pretrained model that lead to minimal distortion in both locality and confidence. This framework aims to minimize detection distortion of network output to maximally maintain detection precision.
  arXiv  Detail & Related papers  (2024-07-02T09:33:32Z)
• Differentiable and Learnable Wireless Simulation with Geometric Transformers [13.538643388955768]
  Wi-GATr is a fully-learnable neural simulation surrogate designed to predict the channel observations based on scene primitives. We evaluate our approach on a range of tasks and find that Wi-GATr is accurate, fast, sample-efficient, and robust to symmetry-induced transformations.
  arXiv  Detail & Related papers  (2024-06-21T09:14:11Z)
• EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes [55.2480439325792]
  We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments.
  arXiv  Detail & Related papers  (2024-05-27T17:19:02Z)
• Efficient and accurate neural field reconstruction using resistive memory [52.68088466453264]
  Traditional signal reconstruction methods on digital computers face both software and hardware challenges. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications.
  arXiv  Detail & Related papers  (2024-04-15T09:33:09Z)
• Physical-Layer Semantic-Aware Network for Zero-Shot Wireless Sensing [74.12670841657038]
  Device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications. Data heterogeneity in wireless signals and data privacy regulation of distributed sensing have been considered as the major challenges that hinder the wide applications of wireless sensing in large area networking systems. We propose a novel zero-shot wireless sensing solution that allows models constructed in one or a limited number of locations to be directly transferred to other locations without any labeled data.
  arXiv  Detail & Related papers  (2023-12-08T13:50:30Z)
• NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping [14.433784957457632]
  We propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction. Our approach achieves state-of-the-art odometry and mapping performance, as well as a strong generalization in large-scale environments utilizing LiDAR data.
  arXiv  Detail & Related papers  (2023-03-19T16:40:36Z)
• TWR-MCAE: A Data Augmentation Method for Through-the-Wall Radar Human Motion Recognition [19.7631142728486]
  We propose a multilink auto-encoding neural network (TWR-MCAE) data augmentation method. The proposed algorithm gets a better peak signal-to-noise ratio (PSNR) Experiments show that the proposed algorithm gets a better peak signal-to-noise ratio (PSNR)
  arXiv  Detail & Related papers  (2023-01-06T12:56:53Z)
• CROMOSim: A Deep Learning-based Cross-modality Inertial Measurement Simulator [7.50015216403068]
  Inertial measurement unit (IMU) data has been utilized in monitoring and assessment of human mobility. To mitigate the data scarcity problem, we design CROMOSim, a cross-modality sensor simulator. It simulates high fidelity virtual IMU sensor data from motion capture systems or monocular RGB cameras.
  arXiv  Detail & Related papers  (2022-02-21T22:30:43Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• SignalNet: A Low Resolution Sinusoid Decomposition and Estimation Network [79.04274563889548]
  We propose SignalNet, a neural network architecture that detects the number of sinusoids and estimates their parameters from quantized in-phase and quadrature samples. We introduce a worst-case learning threshold for comparing the results of our network relative to the underlying data distributions. In simulation, we find that our algorithm is always able to surpass the threshold for three-bit data but often cannot exceed the threshold for one-bit data.
  arXiv  Detail & Related papers  (2021-06-10T04:21:20Z)
• A Compressive Sensing Approach for Federated Learning over Massive MIMO Communication Systems [82.2513703281725]
  Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices. We present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems.
  arXiv  Detail & Related papers  (2020-03-18T05:56:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.