PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery
Jiayue Yuan, Fangting Xie, Guangwen Ouyang, Changhai Ma, Ziyu Wu, Heyu Ding, Quan Wan, Yi Ke
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 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
BibTeX
@inproceedings{aaai2026_presstrackhmrpre,
title = {PressTrack-HMR: Pressure-Based Top-Down Multi-Person Global Human Mesh Recovery},
author = {Jiayue Yuan and Fangting Xie and Guangwen Ouyang and Changhai Ma and Ziyu Wu and Heyu Ding and Quan Wan and Yi Ke and Yuchen Wu and Xiaohui Cai},
booktitle = {AAAI 2026},
year = {2026}
}