{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/can-wifi-estimate-person-pose","title":"Can WiFi Estimate Person Pose?","arxiv_id":"1904.00277","date":"2019-03-30","proceeding":null,"authors":["Fei Wang","Stanislav Panev","Ziyi Dai","Jinsong Han","Dong Huang"],"abstract":"WiFi human sensing has achieved great progress in indoor localization,\nactivity classification, etc. Retracing the development of these work, we have\na natural question: can WiFi devices work like cameras for vision applications?\nIn this paper We try to answer this question by exploring the ability of WiFi\non estimating single person pose. We use a 3-antenna WiFi sender and a\n3-antenna receiver to generate WiFi data. Meanwhile, we use a synchronized\ncamera to capture person videos for corresponding keypoint annotations. We\nfurther propose a fully convolutional network (FCN), termed WiSPPN, to estimate\nsingle person pose from the collected data and annotations. Evaluation on over\n80k images (16 sites and 8 persons) replies aforesaid question with a positive\nanswer. Codes have been made publicly available at\nhttps://github.com/geekfeiw/WiSPPN.","url_abs":"http://arxiv.org/abs/1904.00277v2","url_pdf":"http://arxiv.org/pdf/1904.00277v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"can-wifi-estimate-person-pose","repo_url":"https://github.com/geekfeiw/WiSPPN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"indoor-localization","task_name":"Indoor Localization"},{"task_slug":"rf-based-pose-estimation","task_name":"RF-based Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.00277","atlas_url":"https://app.syntology.ai/?focus=1904.00277","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}