# From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi

arXiv: https://arxiv.org/abs/2012.14066 · HTML: https://mlsanity.com/paper/2012.14066/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [AdaPose: Towards Cross-Site Device-Free Human Pose Estimation with Commodity WiFi](https://mlsanity.com/paper/2309.16964/) (2023)
- [Markerless Multi-view 3D Human Pose Estimation: a survey](https://mlsanity.com/paper/2407.03817/) (2024)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [Towards Robust and Realistic Human Pose Estimation via WiFi Signals](https://mlsanity.com/paper/2501.09411/) (2025)
- [Gesture Recognition with mmWave Wi-Fi Access Points: Lessons Learned](https://mlsanity.com/paper/2306.17062/) (2023)
- [VST-Pose: A Velocity-Integrated Spatiotem-poral Attention Network for Human WiFi Pose Estimation](https://mlsanity.com/paper/2507.09672/) (2025)
- [WiFlow: A Lightweight WiFi-based Continuous Human Pose Estimation Network with Spatio-Temporal Feature Decoupling](https://mlsanity.com/paper/2602.08661/) (2026)
- [MultiFormer: A Multi-Person Pose Estimation System Based on CSI and Attention Mechanism](https://mlsanity.com/paper/2505.22555/) (2025)
- [Intelligent Image Sensing for Crime Analysis: A ML Approach towards Enhanced Violence Detection and Investigation](https://mlsanity.com/paper/2506.13910/) (2025)
- [Seeing Through WiFi: Lightweight Human Pose Estimation with Dynamic Kernel Attention](https://mlsanity.com/paper/2607.03196/) (2026)

## Abstract (arXiv)

In this paper, we present Wi-Mose, the first 3D moving human pose estimation system using commodity WiFi. Previous WiFi-based works have achieved 2D and 3D pose estimation. These solutions either capture poses from one perspective or construct poses of people who are at a fixed point, preventing their wide adoption in daily scenarios. To reconstruct 3D poses of people who move throughout the space rather than a fixed point, we fuse the amplitude and phase into Channel State Information (CSI) images which can provide both pose and position information. Besides, we design a neural network to extract features that are only associated with poses from CSI images and then convert the features into key-point coordinates. Experimental results show that Wi-Mose can localize key-point with 29.7mm and 37.8mm Procrustes analysis Mean Per Joint Position Error (P-MPJPE) in the Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios, respectively, achieving higher performance than the state-of-the-art method. The results indicate that Wi-Mose can capture high-precision 3D human poses throughout the space.