# ProbRadarM3F: mmWave Radar based Human Skeletal Pose Estimation with Probability Map Guided Multi-Format Feature Fusion

arXiv: https://arxiv.org/abs/2405.05164 · HTML: https://mlsanity.com/paper/2405.05164/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 57 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Deep Learning-Based Human Pose Estimation: A Survey](https://mlsanity.com/paper/2012.13392/) (2020)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion](https://mlsanity.com/paper/2307.00724/) (2023)
- [RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization](https://mlsanity.com/paper/2102.05150/) (2021)
- [SMURF: Spatial Multi-Representation Fusion for 3D Object Detection with 4D Imaging Radar](https://mlsanity.com/paper/2307.10784/) (2023)
- [mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D, and Inertial Sensors](https://mlsanity.com/paper/2210.08394/) (2022)
- [Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement](https://mlsanity.com/paper/2007.10599/) (2020)
- [mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar](https://mlsanity.com/paper/2209.05070/) (2022)
- [HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar](https://mlsanity.com/paper/2210.12564/) (2022)
- [RaLiBEV: Radar and LiDAR BEV Fusion Learning for Anchor Box Free Object Detection Systems](https://mlsanity.com/paper/2211.06108/) (2022)
- [Deep Instance Segmentation with Automotive Radar Detection Points](https://mlsanity.com/paper/2110.01775/) (2021)
- [MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation](https://mlsanity.com/paper/2208.10414/) (2022)
- [A Survey of mmWave-based Human Sensing: Technology, Platform and Applications](https://mlsanity.com/paper/2308.03149/) (2023)
- [RFMask: A Simple Baseline for Human Silhouette Segmentation with Radio Signals](https://mlsanity.com/paper/2201.10175/) (2022)
- [Which Framework is Suitable for Online 3D Multi-Object Tracking for Autonomous Driving with Automotive 4D Imaging Radar?](https://mlsanity.com/paper/2309.06036/) (2023)
- [MiliPoint: A Point Cloud Dataset for mmWave Radar](https://mlsanity.com/paper/2309.13425/) (2023)
- [Contrastive Learning for Automotive mmWave Radar Detection Points Based Instance Segmentation](https://mlsanity.com/paper/2203.06553/) (2022)
- [Millimeter Wave Sensing: A Review of Application Pipelines and Building Blocks](https://mlsanity.com/paper/2012.13664/) (2020)

## Built on by (most cited first, 5 of 5)

- [Towards Balanced Multi-Modal Learning in 3D Human Pose Estimation](https://mlsanity.com/paper/2501.05264/) (2025)
- [milliMamba: Specular-Aware Human Pose Estimation via Dual mmWave Radar with Multi-Frame Mamba Fusion](https://mlsanity.com/paper/2512.20128/) (2025)
- [Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets](https://mlsanity.com/paper/2603.14507/) (2026)
- [Can Language Models Understand mmWave Data? Benchmarking Large Language Models for mmWave Radar-Based Human Understanding](https://mlsanity.com/paper/2608.14179/) (2026)
- [Kinematics-Induced Multimodal 3D Human Pose Estimation with Subject-Level Privacy](https://mlsanity.com/paper/2610.02943/) (2026)

## Abstract (arXiv)

Millimeter wave (mmWave) radar is a non-intrusive privacy and relatively convenient and inexpensive device, which has been demonstrated to be applicable in place of RGB cameras in human indoor pose estimation tasks. However, mmWave radar relies on the collection of reflected signals from the target, and the radar signals containing information is difficult to be fully applied. This has been a long-standing hindrance to the improvement of pose estimation accuracy. To address this major challenge, this paper introduces a probability map guided multi-format feature fusion model, ProbRadarM3F. This is a novel radar feature extraction framework using a traditional FFT method in parallel with a probability map based positional encoding method. ProbRadarM3F fuses the traditional heatmap features and the positional features, then effectively achieves the estimation of 14 keypoints of the human body. Experimental evaluation on the HuPR dataset proves the effectiveness of the model proposed in this paper, outperforming other methods experimented on this dataset with an AP of 69.9 %. The emphasis of our study is focusing on the position information that is not exploited before in radar singal. This provides direction to investigate other potential non-redundant information from mmWave rader.