# DensePose From WiFi

arXiv: https://arxiv.org/abs/2301.00250 · HTML: https://mlsanity.com/paper/2301.00250/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 36 references that are in the map.
- First posted on arXiv 31 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Simple Baselines for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1804.06208/) (2018)
- [Convolutional Pose Machines](https://mlsanity.com/paper/1602.00134/) (2016)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [PoseTrack: A Benchmark for Human Pose Estimation and Tracking](https://mlsanity.com/paper/1710.10000/) (2017)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare](https://mlsanity.com/paper/1910.00116/) (2019)
- [Learning Dense Correspondence via 3D-guided Cycle Consistency](https://mlsanity.com/paper/1604.05383/) (2016)
- [Continuous Surface Embeddings](https://mlsanity.com/paper/2011.12438/) (2020)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Learning 3D Human Shape and Pose from Dense Body Parts](https://mlsanity.com/paper/1912.13344/) (2019)
- [DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image](https://mlsanity.com/paper/1903.10153/) (2019)
- [Dense Semantic Correspondence where Every Pixel is a Classifier](https://mlsanity.com/paper/1505.04143/) (2015)
- [Temporal Unet: Sample Level Human Action Recognition using WiFi](https://mlsanity.com/paper/1904.11953/) (2019)
- [Wi2Vi: Generating Video Frames from WiFi CSI Samples](https://mlsanity.com/paper/2001.05842/) (2019)
- [DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension](https://mlsanity.com/paper/2109.00033/) (2021)
- [Characterization of a RS-LiDAR for 3D Perception](https://mlsanity.com/paper/1709.07641/) (2017)

## Built on by (most cited first, 13 of 13)

- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [SUPER: Seated Upper Body Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2407.02455/) (2024)
- [Through-Wall Imaging based on WiFi Channel State Information](https://mlsanity.com/paper/2401.17417/) (2024)
- [hear-your-action: human action recognition by ultrasound active sensing](https://mlsanity.com/paper/2309.08087/) (2023)
- [Radio Frequency Signal based Human Silhouette Segmentation: A Sequential Diffusion Approach](https://mlsanity.com/paper/2407.19244/) (2024)
- [PoseAction: Action Recognition for Patients in the Ward using Deep Learning Approaches](https://mlsanity.com/paper/2310.03288/) (2023)
- [mmID: High-Resolution mmWave Imaging for Human Identification](https://mlsanity.com/paper/2402.00996/) (2024)
- [Reconstructing Depth Images of Moving Objects from Wi-Fi CSI Data](https://mlsanity.com/paper/2503.06458/) (2025)
- [SAWEC: Sensing-Assisted Wireless Edge Computing](https://mlsanity.com/paper/2402.10021/) (2024)
- [SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Open Challenges](https://mlsanity.com/paper/2511.11381/) (2025)
- [RePos: Relative-to-Absolute Pose Factorization for Cross-Environment WiFi-Based 3D Human Pose Estimation](https://mlsanity.com/paper/2607.02986/) (2026)
- [Concealed Handgun Detection using WiFi Signals](https://mlsanity.com/paper/2610.18736/) (2026)

## Abstract (arXiv)

Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.