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dc.contributor.authorZhao, Mingmin
dc.contributor.authorLiu, Yingcheng
dc.contributor.authorRaghu, Aniruddh
dc.contributor.authorZhao, Hang
dc.contributor.authorLi, Tianhong
dc.contributor.authorTorralba, Antonio
dc.contributor.authorKatabi, Dina
dc.date.accessioned2021-02-03T23:08:49Z
dc.date.available2021-02-03T23:08:49Z
dc.date.issued2020-02
dc.date.submitted2019-10
dc.identifier.isbn9781728148038
dc.identifier.issn2380-7504
dc.identifier.urihttps://hdl.handle.net/1721.1/129671
dc.description.abstractThis paper presents RF-Avatar, a neural network model that can estimate 3D meshes of the human body in the presence of occlusions, baggy clothes, and bad lighting conditions. We leverage that radio frequency (RF) signals in the WiFi range traverse clothes and occlusions and bounce off the human body. Our model parses such radio signals and recovers 3D body meshes. Our meshes are dynamic and smoothly track the movements of the corresponding people. Further, our model works both in single and multi-person scenarios. Inferring body meshes from radio signals is a highly under-constrained problem. Our model deals with this challenge using: 1) a combination of strong and weak supervision, 2) a multi-headed self-attention mechanism that attends differently to temporal information in the radio signal, and 3) an adversarially trained temporal discriminator that imposes a prior on the dynamics of human motion. Our results show that RF-Avatar accurately recovers dynamic 3D meshes in the presence of occlusions, baggy clothes, bad lighting conditions, and even through walls.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/iccv.2019.01021en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleThrough-Wall Human Mesh Recovery Using Radio Signalsen_US
dc.typeArticleen_US
dc.identifier.citationZhao, Mingmin et al. "Through-Wall Human Mesh Recovery Using Radio Signals." 2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 2019, Seoul, Korea, Institute of Electrical and Electronics Engineers, February 2020 © 2019 IEEEen_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.relation.journal2019 IEEE/CVF International Conference on Computer Vision (ICCV)en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2020-12-23T16:33:54Z
dspace.orderedauthorsZhao, M; Liu, Y; Raghu, A; Zhao, H; Li, T; Torralba, A; Katabi, Den_US
dspace.date.submission2020-12-23T16:34:00Z
mit.licenseOPEN_ACCESS_POLICY


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