Intelligent Carpet: Inferring 3D Human Pose from Tactile Signals
Abstract
Daily human activities, e.g., locomotion, exercises, and resting, are heavily guided by the tactile interactions between the human and the ground. In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input. We build a low-cost, high-density, large-scale intelligent carpet, which enables the real-time recordings of human-floor tactile interactions in a seamless manner. We collect a synchronized tactile and visual dataset on various human activities. Employing a state-of-the-art camera-based pose estimation model as supervision, we design and implement a deep neural network model to infer 3D human poses using only the tactile information. Our pipeline can be further scaled up to multi-person pose estimation. We evaluate our system and demonstrate its potential applications in diverse fields.
Cite
Text
Luo et al. "Intelligent Carpet: Inferring 3D Human Pose from Tactile Signals." Conference on Computer Vision and Pattern Recognition, 2021. doi:10.1109/CVPR46437.2021.01110Markdown
[Luo et al. "Intelligent Carpet: Inferring 3D Human Pose from Tactile Signals." Conference on Computer Vision and Pattern Recognition, 2021.](https://mlanthology.org/cvpr/2021/luo2021cvpr-intelligent/) doi:10.1109/CVPR46437.2021.01110BibTeX
@inproceedings{luo2021cvpr-intelligent,
title = {{Intelligent Carpet: Inferring 3D Human Pose from Tactile Signals}},
author = {Luo, Yiyue and Li, Yunzhu and Foshey, Michael and Shou, Wan and Sharma, Pratyusha and Palacios, Tomas and Torralba, Antonio and Matusik, Wojciech},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2021},
pages = {11255-11265},
doi = {10.1109/CVPR46437.2021.01110},
url = {https://mlanthology.org/cvpr/2021/luo2021cvpr-intelligent/}
}