{"ID":2842728,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.09147","arxiv_id":"2511.09147","title":"PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery","abstract":"Multi-person global human mesh recovery (HMR) is crucial for understanding crowd dynamics and interactions. Traditional vision-based HMR methods sometimes face limitations in real-world scenarios due to mutual occlusions, insufficient lighting, and privacy concerns. Human-floor tactile interactions offer an occlusion-free and privacy-friendly alternative for capturing human motion. Existing research indicates that pressure signals acquired from tactile mats can effectively estimate human pose in single-person scenarios. However, when multiple individuals walk randomly on the mat simultaneously, how to distinguish intermingled pressure signals generated by different persons and subsequently acquire individual temporal pressure data remains a pending challenge for extending pressure-based HMR to the multi-person situation. In this paper, we present \\textbf{PressTrack-HMR}, a top-down pipeline that recovers multi-person global human meshes solely from pressure signals. This pipeline leverages a tracking-by-detection strategy to first identify and segment each individual's pressure signal from the raw pressure data, and subsequently performs HMR for each extracted individual signal. Furthermore, we build a multi-person interaction pressure dataset \\textbf{MIP}, which facilitates further research into pressure-based human motion analysis in multi-person scenarios. Experimental results demonstrate that our method excels in multi-person HMR using pressure data, with 89.2 $mm$ MPJPE and 112.6 $mm$ WA-MPJPE$_{100}$, and these showcase the potential of tactile mats for ubiquitous, privacy-preserving multi-person action recognition. Our dataset \u0026 code are available at https://github.com/Jiayue-Yuan/PressTrack-HMR.","short_abstract":"Multi-person global human mesh recovery (HMR) is crucial for understanding crowd dynamics and interactions. Traditional vision-based HMR methods sometimes face limitations in real-world scenarios due to mutual occlusions, insufficient lighting, and privacy concerns. Human-floor tactile interactions offer an occlusion-f...","url_abs":"https://arxiv.org/abs/2511.09147","url_pdf":"https://arxiv.org/pdf/2511.09147v2","authors":"[\"Jiayue Yuan\",\"Fangting Xie\",\"Guangwen Ouyang\",\"Changhai Ma\",\"Ziyu Wu\",\"Heyu Ding\",\"Quan Wan\",\"Yi Ke\",\"Yuchen Wu\",\"Xiaohui Cai\"]","published":"2025-11-12T09:33:44Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":607156,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2842728,"paper_url":"https://arxiv.org/abs/2511.09147","paper_title":"PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery","repo_url":"https://github.com/Jiayue-Yuan/PressTrack-HMR","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}