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Argus: Multi-View Egocentric Human Mesh Reconstruction Based on Stripped-Down Wearable mmWave Add-on

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Title: Argus: Multi-View Egocentric Human Mesh Reconstruction Based on Stripped-Down Wearable mmWave Add-on
Authors: Duan, Di, Lyu, Shengzhe, Yuan, Mu, Xue, Hongfei, Li, Tianxing, Xu, Weitao, Wu, Kaishun, Xing, Guoliang
Publication Year: 2024
Collection: Computer Science
Subject Terms: Computer Science - Human-Computer Interaction, C.3
More Details: In this paper, we propose Argus, a wearable add-on system based on stripped-down (i.e., compact, lightweight, low-power, limited-capability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with conventional frontal-view mmWave sensing solutions, it addresses several pain points, such as restricted sensing range, occlusion, and the multipath effect caused by surroundings. To overcome the limited capabilities of the stripped-down mmWave radars (with only one transmit antenna and three receive antennas), we tackle three main challenges and propose a holistic solution, including tailored hardware design, sophisticated signal processing, and a deep neural network optimized for high-dimensional complex point clouds. Extensive evaluation shows that Argus achieves performance comparable to traditional solutions based on high-capability mmWave radars, with an average vertex error of 6.5 cm, solely using stripped-down radars deployed in a multi-view configuration. It presents robustness and practicality across conditions, such as with unseen users and different host devices.
Comment: 15 pages, 25 figures
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2411.00419
Accession Number: edsarx.2411.00419
Database: arXiv
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  Data: Argus: Multi-View Egocentric Human Mesh Reconstruction Based on Stripped-Down Wearable mmWave Add-on
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  Data: <searchLink fieldCode="AR" term="%22Duan%2C+Di%22">Duan, Di</searchLink><br /><searchLink fieldCode="AR" term="%22Lyu%2C+Shengzhe%22">Lyu, Shengzhe</searchLink><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Mu%22">Yuan, Mu</searchLink><br /><searchLink fieldCode="AR" term="%22Xue%2C+Hongfei%22">Xue, Hongfei</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Tianxing%22">Li, Tianxing</searchLink><br /><searchLink fieldCode="AR" term="%22Xu%2C+Weitao%22">Xu, Weitao</searchLink><br /><searchLink fieldCode="AR" term="%22Wu%2C+Kaishun%22">Wu, Kaishun</searchLink><br /><searchLink fieldCode="AR" term="%22Xing%2C+Guoliang%22">Xing, Guoliang</searchLink>
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  Data: In this paper, we propose Argus, a wearable add-on system based on stripped-down (i.e., compact, lightweight, low-power, limited-capability) mmWave radars. It is the first to achieve egocentric human mesh reconstruction in a multi-view manner. Compared with conventional frontal-view mmWave sensing solutions, it addresses several pain points, such as restricted sensing range, occlusion, and the multipath effect caused by surroundings. To overcome the limited capabilities of the stripped-down mmWave radars (with only one transmit antenna and three receive antennas), we tackle three main challenges and propose a holistic solution, including tailored hardware design, sophisticated signal processing, and a deep neural network optimized for high-dimensional complex point clouds. Extensive evaluation shows that Argus achieves performance comparable to traditional solutions based on high-capability mmWave radars, with an average vertex error of 6.5 cm, solely using stripped-down radars deployed in a multi-view configuration. It presents robustness and practicality across conditions, such as with unseen users and different host devices.<br />Comment: 15 pages, 25 figures
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              Y: 2024
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