# HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar

arXiv: https://arxiv.org/abs/2210.12564 · HTML: https://mlsanity.com/paper/2210.12564/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 22 Oct 2022.

## Builds on (most cited first, 17 of 17)

- [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)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Semantic Graph Convolutional Networks for 3D Human Pose Regression](https://mlsanity.com/paper/1904.03345/) (2019)
- [Human Pose Regression with Residual Log-likelihood Estimation](https://mlsanity.com/paper/2107.11291/) (2021)
- [Single-Stage Multi-Person Pose Machines](https://mlsanity.com/paper/1908.09220/) (2019)
- [mm-Pose: Real-Time Human Skeletal Posture Estimation using mmWave Radars and CNNs](https://mlsanity.com/paper/1911.09592/) (2019)
- [Person-in-WiFi: Fine-grained Person Perception using WiFi](https://mlsanity.com/paper/1904.00276/) (2019)
- [Point-Set Anchors for Object Detection, Instance Segmentation and Pose Estimation](https://mlsanity.com/paper/2007.02846/) (2020)
- [Graph-PCNN: Two Stage Human Pose Estimation with Graph Pose Refinement](https://mlsanity.com/paper/2007.10599/) (2020)
- [Simple Pose: Rethinking and Improving a Bottom-up Approach for Multi-Person Pose Estimation](https://mlsanity.com/paper/1911.10529/) (2019)
- [Learning Longterm Representations for Person Re-Identification Using Radio Signals](https://mlsanity.com/paper/2004.01091/) (2020)
- [mmPose-NLP: A Natural Language Processing Approach to Precise Skeletal Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2107.10327/) (2021)

## Built on by (most cited first, 30 of 31)

- [Diffusion Model is a Good Pose Estimator from 3D RF-Vision](https://mlsanity.com/paper/2403.16198/) (2024)
- [RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark](https://mlsanity.com/paper/2407.13930/) (2024)
- [MiliPoint: A Point Cloud Dataset for mmWave Radar](https://mlsanity.com/paper/2309.13425/) (2023)
- [MMVR: Millimeter-wave Multi-View Radar Dataset and Benchmark for Indoor Perception](https://mlsanity.com/paper/2406.10708/) (2024)
- [M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2512.12378/) (2025)
- [RETR: Multi-View Radar Detection Transformer for Indoor Perception](https://mlsanity.com/paper/2411.10293/) (2024)
- [ProbRadarM3F: mmWave Radar based Human Skeletal Pose Estimation with Probability Map Guided Multi-Format Feature Fusion](https://mlsanity.com/paper/2405.05164/) (2024)
- [SUPER: Seated Upper Body Pose Estimation using mmWave Radars](https://mlsanity.com/paper/2407.02455/) (2024)
- [Human Modelling and Pose Estimation Overview](https://mlsanity.com/paper/2406.19290/) (2024)
- [mmEgoHand: Egocentric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMU](https://mlsanity.com/paper/2501.13805/) (2025)
- [RAPTR: Radar-based 3D Pose Estimation using Transformer](https://mlsanity.com/paper/2511.08387/) (2025)
- [milliMamba: Specular-Aware Human Pose Estimation via Dual mmWave Radar with Multi-Frame Mamba Fusion](https://mlsanity.com/paper/2512.20128/) (2025)
- [Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning](https://mlsanity.com/paper/2311.02815/) (2023)
- [MAROON: A Dataset for the Joint Characterization of Near-Field High-Resolution Radio-Frequency and Optical Depth Imaging Techniques](https://mlsanity.com/paper/2411.00527/) (2024)
- [mmWEAVER: Environment-Specific mmWave Signal Synthesis from a Photo and Activity Description](https://mlsanity.com/paper/2512.11894/) (2025)
- [WiCompass: Oracle-driven Data Scaling for mmWave Human Pose Estimation](https://mlsanity.com/paper/2602.18726/) (2026)
- [Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets](https://mlsanity.com/paper/2603.14507/) (2026)
- [DGHMesh: A Large-scale Dual-radar mmWave Dataset and Generalization-focused Benchmark for Human Mesh Reconstruction](https://mlsanity.com/paper/2604.22827/) (2026)
- [A Two-Stage Motion-Aware Framework for mmWave-based Human Mesh Recovery](https://mlsanity.com/paper/2605.08530/) (2026)
- [Wave2Body: Rethinking mmWave Human Pose Estimation as Radar-to-Body Token Translation](https://mlsanity.com/paper/2607.18875/) (2026)
- [Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool](https://mlsanity.com/paper/2309.08844/) (2023)
- [RadProPoser: Probabilistic Radar Tensor Human Pose Estimation That Knows Its Limits](https://mlsanity.com/paper/2508.03578/) (2025)
- [PCFEx: Point Cloud Feature Extraction for Graph Neural Networks](https://mlsanity.com/paper/2603.08540/) (2026)
- [mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud](https://mlsanity.com/paper/2603.08551/) (2026)
- [UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment](https://mlsanity.com/paper/2605.08288/) (2026)
- [DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition](https://mlsanity.com/paper/2605.09604/) (2026)
- [Privacy-Preserving Industrial Ergonomics: mmWave-Based Automated REBA Scoring and Pose Estimation](https://mlsanity.com/paper/2607.02611/) (2026)
- [Learning Biomechanically Plausible Human Motion from Sparse Radar Point Clouds](https://mlsanity.com/paper/2608.03637/) (2026)
- [Vision Meets WiFi: Physics-Grounded Estimation of Volumetric Mechanical Properties](https://mlsanity.com/paper/2608.07726/) (2026)
- [You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows](https://mlsanity.com/paper/2608.09579/) (2026)

## Abstract (arXiv)

This paper introduces a novel human pose estimation benchmark, Human Pose with Millimeter Wave Radar (HuPR), that includes synchronized vision and radio signal components. This dataset is created using cross-calibrated mmWave radar sensors and a monocular RGB camera for cross-modality training of radar-based human pose estimation. There are two advantages of using mmWave radar to perform human pose estimation. First, it is robust to dark and low-light conditions. Second, it is not visually perceivable by humans and thus, can be widely applied to applications with privacy concerns, e.g., surveillance systems in patient rooms. In addition to the benchmark, we propose a cross-modality training framework that leverages the ground-truth 2D keypoints representing human body joints for training, which are systematically generated from the pre-trained 2D pose estimation network based on a monocular camera input image, avoiding laborious manual label annotation efforts. The framework consists of a new radar pre-processing method that better extracts the velocity information from radar data, Cross- and Self-Attention Module (CSAM), to fuse multi-scale radar features, and Pose Refinement Graph Convolutional Networks (PRGCN), to refine the predicted keypoint confidence heatmaps. Our intensive experiments on the HuPR benchmark show that the proposed scheme achieves better human pose estimation performance with only radar data, as compared to traditional pre-processing solutions and previous radio-frequency-based methods.