Wi-Fi signals, in contrast to cameras, offer privacy protection and occlusion resilience for some practical scenarios such as smart homes, elderly care, and virtual reality. Recent years have seen remarkable progress in the estimation of single-person 2D pose, single-person 3D pose, and multi-person 2D pose. This paper takes a step forward by introducing Person-in-WiFi 3D, a pioneering Wi-Fi system that accomplishes multi-person 3D pose estimation. Person-in-WiFi 3D has two main updates. Firstly, it has a greater number of Wi-Fi devices to enhance the capability for capturing spatial reflections from multiple individuals. Secondly, it leverages the Transformer for end-to-end estimation. Compared to its predecessor, Person-in-WiFi 3D is storage-efficient and fast. We deployed a proof-of-concept system in 4m × 3.5m areas and collected a dataset of over 97K frames with seven volunteers. Person-in-WiFi 3D attains 3D joint localization errors of 91.7mm (1-person), 108.1mm (2-person), and 125.3mm (3-person), comparable to cameras and millimeter-wave radars.

Links to our Project: Person-in-WiFi 3D: End-to-End Multi-Person 3D Pose Estimation with Wi-Fi

Links to our Code : Person-in-WiFi 3D repo

• Linux
• Python 3.7+
• PyTorch 1.8+
• CUDA 10.1+
• MMCV
• MMDetection

Please see get_started.md for the basic usage of Opera.

Opera is an open source project built upon OpenMMLab. We appreciate all the contributors who implement this flexible and efficient toolkits.

.
│  LICENSE
│  README.md
│  requirements.txt
│  setup.cfg
│  setup.py
├─data
│  ├─wifipose
│  │  ├─train_data
│  │  │     ├─ csi
│  │  │     ├─ keypoint
│  │  │     ├─ train_data_list.txt
│  │  ├─test_data
│  │  │     ├─ csi
│  │  │     ├─ keypoint
│  │  │     ├─ test_data_list.txt
├─config
│  ├─base
│  ├─wifi
│  │  ├─petr_wifi.py
├─docs
│  ├─get_started.md
├─opera
│  ├─apis
│  ├─core
│  ├─datasets
│  ├─models
│  ├─__init__.py
│  ├─version.py
├─requirements
│  ├─build.txt
│  ├─docs.txt
│  ├─mminstall.txt
│  ├─optional.txt
│  ├─readthedocs.txt
│  ├─runtime.txt
│  ├─tests.txt
├─result
├─third_party
│  ├─mmcv
│  ├─mmdet
├─tools
│  ├─dataset_converters
│  ├─dist_test.sh
│  ├─dist_train.sh
│  ├─eval_metric.py
│  ├─test_all.sh
│  ├─test.py
│  ├─train.py

If you find our works useful in your research, please consider citing:

@inproceedings{person3dyan,
  title={Person-in-WiFi 3D: End-to-End Multi-Person 3D Pose Estimation with Wi-Fi },
  author={Yan, Kangwei and Wang, Fei and Qian, Bo and Ding, Han and Han, Jinsong and Wei, Xing},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year = {2024}
}

@inproceedings{shi2022end,
  title={End-to-End Multi-Person Pose Estimation With Transformers},
  author={Shi, Dahu and Wei, Xing and Li, Liangqi and Ren, Ye and Tan, Wenming},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={11069--11078},
  year={2022}
}

This project is released under the Apache 2.0 license.